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Marriott Marquis Houston
Houston, Texas, United States
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Conference 11315
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Image-Guided Procedures, Robotic Interventions, and Modeling
Sunday - Wednesday 16 - 19 February 2020
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1: Calibration and Tracking for Image-guided Navigation
2: AI Methods for Image-guided Therapy
Sunday/Monday Poster Viewing
3: Image-guided Orthopedic Applications
4: Ultrasound Imaging and Image Guidance: Joint Session with Conferences 11315 and 11319
5: Image-guided Neurosurgical Interventions
6: MD Anderson Invited Session: Advances in Image-Guided, Data-Driven Interventions
7: AI-based Methods for Tissue Classification: Diagnosis and Therapy Applications
8: Keynote Presentation
9: Augmented Reality for Image-guided Therapy
Awards and Plenary Session
Monday Poster Session
10: Novel Imaging Technologies for Interventional Guidance
11: Video and Optical Methods for Imaging
12: Robot-assisted Image-guided Therapy
13: Modeling Applications for Image-guided Therapeutics
14: AI-based Image Segmentation and Feature Detection
15: Journal of Medical Imaging Special Section on Interventional and Surgical Data Science
Abstract Due:
Author Notification:
Manuscript Due Date:
Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. (United States)
Cristian A. Linte, Rochester Institute of Technology (United States)
Purang Abolmaesumi, The Univ. of British Columbia (Canada)
Matthieu Chabanas, Univ. Grenoble Alpes (France)
Elvis C. S. Chen, Robarts Research Institute (Canada)
Sandrine de Ribaupierre, Western Univ. (Canada)
Gabor Fichtinger, Queen's Univ. (Canada)
Ryan J. Halter, Thayer School of Engineering at Dartmouth (United States)
David Hawkes, Univ. College London (United Kingdom)
David R. Haynor, Univ. of Washington (United States)
William E. Higgins, The Pennsylvania State Univ. (United States)
David R. Holmes, Mayo Clinic (United States)
Pierre Jannin, Univ. de Rennes 1 (France)
David M. Kwartowitz, Grand Canyon Univ. (United States)
Shuo Li, Western Univ. (Canada)
Michael I. Miga, Vanderbilt Univ. (United States)
Program Committee continued...
Kensaku Mori, Nagoya Univ. (Japan)
Parvin Mousavi, Queen's Univ. (Canada)
Jack H. Noble, Vanderbilt Univ. (United States)
Maryam E. Rettmann, Mayo Clinic (United States)
Frank Sauer, Siemens Healthineers (United States)
Eric J. Seibel, Univ. of Washington (United States)
Guy Shechter, Philips Healthcare (United States)
Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
Amber L. Simpson, Queen's Univ. (Canada)
Stefanie Speidel, National Ctr. for Tumor Diseases Dresden (Germany)
Tamas Ungi, Lab. for Percutaneous Surgery (Canada)
Satish E. Viswanath, Case Western Reserve Univ. (United States)
Robert J. Webster, Vanderbilt Univ. (United States)
Andrew D. Wiles, Northern Digital Inc. (Canada)
Ivo Wolf, Hochschule Mannheim (Germany)
Ziv R. Yaniv, National Institute of Allergy and Infectious Diseases (United States)
Sunday 16 February Show All Abstracts
Calibration and Tracking for Image-guided Navigation
Location: Hunters Creek
Session Chairs:
Elvis C. S. Chen, Robarts Research Institute (Canada) ;
Miniature C-arm simulator using wireless accelerometer based tracking
Paper 11315-1
Time: 8:00 AM - 8:20 AM
Author(s): Daniel R. Allen, John T. Moore, Robarts Research Institute (Canada); Collin Clarke, London Health Sciences Ctr. (Canada); Terry M. Peters, Elvis C. S. Chen, Robarts Research Institute (Canada)
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The C-arm has enabled minimally invasive procedures to be performed under X-ray or fluoroscopy guidance. However, training for these procedures can often expose the trainee to a prolonged dosage of radiation. We have developed a 10:1 scaled down C-arm simulator using wireless accelerometers for tracking and a laptop to display the Digitally Reconstructed radiographs. A user study was conducted in which participants were evaluated on the accuracy of their C-arm placement for each of three standard views used in Lumbar Spine Injection procedures. The experimental group achieved more accurate results than the control group and the overall feedback of the system was high based on a Likert-scale questionnaire filled out by each participant.
Pivot calibration concept for sensor attached mobile c-arms
Author(s): Sing Chun Lee, Johns Hopkins Univ. (United States); Matthias Seibold, Technische Univ. München (Germany), Univ. Hospital Balgrist (Switzerland); Philipp Fürnstahl, Mazda Farshad, Univ. Hospital Balgrist (Switzerland); Nassir Navab, Johns Hopkins Univ. (United States), Technische Univ. München (Germany)
In order to enable augmentation of real-time camera feedback to the medical data from a mobile C-arm, a precise one-time calibration is required to co-register the two spaces. Current technique requires taking radiation in overlapping views or time-consuming calibration procedures for this purpose. We propose a simple and radiation free approach by exploiting the motion geometry of the device. We formulate the problem as an extended pivot calibration and propose a novel algorithm that decomposes the non-linear problem into 2D linear sub-problems. Our method is robust to noise, independent to outliers, and invariant to the camera coordinate system.
3D catheter guidance including shape sensing for endovascular navigation
Author(s): Sonja Jäckle, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany); Verónica García-Vázquez, Felix von Haxthausen, Univ. zu Lübeck (Germany); Tim Eixmann, Medizinisches Laserzentrum Lübeck GmbH (Germany); Malte Maria Sieren, Universitätsklinikum Schleswig-Holstein (Germany); Hinnerk Schulz-Hildebrandt, Gereon Hüttmann, Medizinisches Laserzentrum Lübeck GmbH (Germany), Univ. zu Lübeck (Germany), Deutsches Zentrum für Lungenforschung, DZL (Germany); Floris Ernst, Univ. zu Lübeck (Germany); Markus Kleemann, Universitätsklinikum Schleswig-Holstein (Germany); Torben Paetz, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Currently, fluoroscopy and conventional digital subtraction angiography are used for imaging guidance in endovascular procedures, which has several disadvantages. In this study, a catheter prototype containing a multicore fiber with fiber Bragg gratings and three electromagnetic sensors was built for 3D catheter navigation. The shape was reconstructed with the multicore fiber, and the orientation and position was given by the electromagnetic sensors. A spatial calibration between both tracking systems was made and used for shape localization. The experiments showed that an accurate shape localization with a multicore fiber and three electromagnetic sensors is possible.
Feasibility of 3D motion-compensated needle guidance for TIPS procedures
Author(s): Martin G. Wagner, Sarvesh Periyasamy, Michael A. Speidel, Paul F. Laeseke, Univ. of Wisconsin-Madison (United States)
We propose a motion-compensated 3D needle guidance system for TIPS procedures, which require navigating a needle from a hepatic vein through the parenchyma of the liver and puncturing into a portal vein. The system generates a respiratory motion model of the portal vein using a 3D DSA and a contrast enhanced 2D x-ray sequence. The 3D needle position is reconstructed from biplane x-ray images and the respiratory motion is tracked based on diaphragm motion. A display combining a 3D rendering of the needle and the motion-compensated vasculature could result in fewer unnecessary needle passes and therefore shorter procedure times and lower risk to the patient.
Towards electromagnetic tracking of J-tip guidewire: precision assessment of sensors during bending tests
Author(s): Roberta Piazza, EndoCAS, Univ. di Pisa (Italy), Robarts Research Institute (Canada); Hareem Nisar, Robarts Research Institute (Canada), Western Univ. (Canada); John Moore, Robarts Research Institute (Canada); Sara Condino, EndoCAS, Univ. di Pisa (Italy); Mauro Ferrari, EndoCAS (Italy); Vincenzo Ferrari, EndoCAS, Univ. di Pisa (Italy); Terry Peters, Elvis Chen, Robarts Research Institute (Canada)
Coffee Break 9:40 AM - 10:10 AM
AI Methods for Image-guided Therapy
Jack H. Noble, Vanderbilt Univ. (United States) ;
Validation of a metal artifact reduction method based on 3d conditional GANs for CT images of the ear
Author(s): Jianing Wang, Srijata Chakravorti, Vanderbilt Univ. (United States); Yiyuan Zhao, Siemens Medical Solutions (United States); Jack H. Noble, Benoit M. Dawant, Vanderbilt Univ. (United States)
We evaluate the effect of our recently proposed image synthesis method based on 3D conditional generative adversarial networks, which is designed to remove artifacts introduced by the metallic implant in the CT images of cochlear implants (CI) receivers, on the output of our image-guided CI programming (IGCIP) methods that were developed to assist audiologists with configuration of the CI electrodes postoperatively. Configurations of CI electrodes obtained using the real and synthetic images are compared, in 22/87 cases synthetic image lead to the same results as the real images, visual assessment of solutions is required to compare those that differ.
Ultrasound image simulation with generative adversarial network
Author(s): Grace Pigeau, Lydia Elbatarny, Victoria Wu, Abigael Schonewille, Gabor Fichtinger, Tamas Ungi, Queen's Univ. (Canada)
It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to ultrasound images. However, recent developments in deep neural networks, especially generative adversarial networks (GANs), have provided practical tools to simulate complex images. We propose to use and evaluate a GAN for simulating kidney ultrasound images from anatomical label-maps. To achieve our goal, kidney ultrasounds were collected and relevant anatomy was segmented. A GAN was then trained to generate ultrasound images from segmented label-maps. Finally, a survey of participants with ultrasound experience was conducted to assess the realism of the generated images. Based on the survey results, GANs appear to be a promising method of simulating realistic ultrasound images.
Image registration with deep probabilistic classifiers: application in radiation therapy
Author(s): Alireza Sedghi, Gregory Salomons, Jean-David Jutras, John Schreiner, Queen's Univ. (Canada); William Wells, Brigham and Women's Hospital (United States); Parvin Mousavi, Queen's Univ. (Canada)
We present the application of deep multi-class classifiers for registration of the pre-radiation image (CBCT) to the treatment planning image (planCT) in Radiation Therapy (RT). We train a multi-class classifier on different classes of displacement between 3D patches of images and use it for registration. As the initial displacement between images might be large, we train multiple classifiers for different resolutions of the data to capture larger displacements in coarser resolutions. We show that having only a few patients, the deep multi-class classifiers enable an accurate and fast rigid registration for CBCT to planCT even with significantly different fields of view. Our work lays the foundation for deformable image registration and prediction of registration uncertainty which can be utilized for adaptive RT.
Automatic labeling of respiratory phases and detection of abnormal respiratory signals in free-breathing thoracic dynamic MR image acquisitions based on deep learning
Author(s): Changjian Sun, Jayaram K. Udupa, Yubing Tong, Caiyun Wu, Penn Medicine (United States); Joseph McDonough, The Children's Hospital of Philadelphia (United States); Drew Torigian, Penn Medicine (United States); Patrick Cahill, The Children's Hospital of Philadelphia (United States)
In this paper, we designed a framework for signal processing based on deep learning including automatic labeling of respiratory signals and detection of abnormal respiratory signals. CNN is used to extract the motion characteristics of each slice in the respiratory sequence and complete the classification of the exhalation and inhalation process. LSTM completes the identification of abnormal respiratory signals depending on the characteristics of abnormal breathing motion. Experiments have verified that our framework achieves an accuracy with a mean error of less than one time point. The accuracy of the signal classification reaches 80.0%.
Image-based deformable motion compensation in cone-beam CT: translation to clinical studies in interventional body radiology
Paper 11315-10
Author(s): Sarah Capostagno, Alejandro Sisniega, Joseph W. Stayman, Johns Hopkins Univ. (United States); Tina Ehtiati, Siemens Medical Solutions USA, Inc. (United States); Clifford R. Weiss, Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including simulation, phantom, cadaver, and animal studies as precursors to clinical studies. Overall, the studies demonstrate robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., simulation, mobile C-arm, and clinical C-arm) and other confounding factors in experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional body radiology.
Stabilized ultrasound imaging of a moving object using 2D B-mode images and convolutional neural network
Author(s): Tian Xie, Mahya Shahbazi, Yixuan Wu, Russell H. Taylor, Emad M. Boctor, Johns Hopkins Univ. (United States)
This work presents a co-robotic ultrasound imaging system with a 1D linear array to track the lesion undergoing physiological motions. The approach embeds the in-plane and out-of-plane transformation estimation in a proportional joint velocity controller to minimize the 6 DOF transformation error. Specifically, we proposed a new method to estimate the out-of-plane translation using convolutional neural network based on speckle decorrelation. The network is trained on anatomical featureless gray-scale B-mode images and is generalized to different tissue phantoms. The tracking algorithm is validated in simulation with mimicked respiratory motion and shows feasibility of a stabilized ultrasound guidance for biopsy.
Location: Salon D/E
Posters will be on display Sunday and Monday with extended viewing until 9:00 pm on Sunday. The poster session with authors in attendance will be Monday evening from 5:30 to 7:00 pm. Award winners will be identified with ribbons during the reception. Award announcement times are listed in the conference schedule.
Lunch Break 12:10 PM - 1:20 PM
Image-guided Orthopedic Applications
David R. Haynor, Univ. of Washington (United States) ;
Infrared image-guidance for intraoperative assessment of limb length discrepancy during total hip arthroplasty orthopedic procedures
Author(s): Jerry Yan, Akash Chaurasia, Katie McCarren, Robert Li, Claire State, Hannah Takasuka, Evan Bender, Aditi Jithendra, Matthew Hill, Julius Oni, Amir Manbachi, Johns Hopkins Univ. (United States)
Total hip arthroplasty procedures have been identified as high-volume procedures with growing prevalence. Intraoperatively, orthopedic surgeons largely rely on visual and tactile cues to ensure that an excessive limb length discrepancy (LLD) is not introduced from implant selection and positioning. We have investigated the feasibility of using a commercially available infrared motion controller to measure patient anatomy as an accurate, minimally invasive, and affordable method of measuring LLD that is easily integrable into the surgical workflow. This paper outlines a novel application of an infrared (IR) motion controller to function as an optical tracker and to assess limb length discrepancy.
Three-dimensional ultrasound for monitoring knee inflammation and cartilage damage in osteoarthritis and rheumatoid arthritis
Author(s): Sam Papernick, Derek J. Gillies, Tom Appleton, Aaron Fenster, Western Univ. (Canada)
The two most common chronic inflammatory and chronic joint diseases are rheumatoid arthritis (RA) and osteoarthritis (OA) respectively. Currently these diseases are diagnosed using x-ray, magnetic resonance imaging (MRI), or ultrasound. However, these imaging modalities present difficulties with interpreting three-dimensional anatomy and observing soft tissue degradation over time. Our solution to the limitations imposed by x-ray, MRI, and conventional ultrasound is the use of three-dimensional ultrasound (3DUS) imaging for providing a bedside diagnosis of RA and OA. 3DUS was able to reproduce the necessary anatomical and diagnostic information achieved with MRI while maintaining scan times feasible for bedside care.
Multi-body registration for fracture reduction and guidance of orthopaedic trauma surgery
Author(s): Runze Han, Ali Uneri, Pengwei Wu, Rohan C. Vijayan, Prasad Vagdargi, Michael D. Ketcha, Niral Sheth, Johns Hopkins Univ. (United States); Sebastian Vogt, Gerhard Kleinszig, Siemens Healthineers (Germany); Greg M. Osgood, The Johns Hopkins Hospital (United States); Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
Pelvic trauma presents a challenge to orthopaedic surgeons in performing reduction and fixation while navigating through complex anatomy under 2D fluoroscopy guidance, requiring extensive manual planning, trial-and-error and long fluoroscopy time. In this work, we propose a multi-body image-based 3D-3D and 3D-2D registration for automatic reduction planning using preoperative CT and fluoroscopy-guided navigation using 2D fluoroscopy alone. A leave-one-out study was performed over 40 pelvic atlas members, achieving ~4 mm error in planning and ~2 mm error in 3D navigation. The method demonstrated capability of preoperative planning, trackerless navigation, surgical guidance, and quality assurance in pelvic trauma orthopaedic surgery.
Calibration and registration of a freehand video-guided surgical drill for orthopaedic trauma
Author(s): Prasad Vagdargi, Ali Uneri, Niral Sheth, Alejandro Sisniega, Tharindu De Silva, Johns Hopkins Univ. (United States); Greg M. Osgood, Johns Hopkins Medicine (United States); Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
A computer-vision based navigation system for guidewire insertion is proposed, which provides augmented visualization of target trajectories with respect to CT or single fluoroscopic images. The system uses a drill-mounted camera to detect and track poses of optical-radiographic markers for registration of fluoroscopy with camera and CT imaging. The camera, drill axis, and tooltip was cal-ibrated with sub-mm accuracy resulting in ~0.4° error for a guidewire of 15 cm length. The system consistently presented <2% error across all aspects. The work demonstrates the first steps toward an accurate and robust system that is compatible with rapid work-flow in trauma surgery.
MRI-compatible needle guidance toolkit that streamlines arthrography procedures: phantom accuracy study
Author(s): Reza Monfaredi, Pavel Yarmolenko, Children's National Health System (United States); Eung Joo Lee, Univ. of Maryland, College Park (United States); Viktoriya Beskin, Kevin Cleary, Karun Sharma, Children's National Health System (United States)
In this paper we introduce a novel and enabling MRI-compatible needle guidance toolkit that streamlines the Arthrography procedure and eliminates ionizing radiation exposure to patients. We developed a flexible grid template with unique patterns. This MRI-visible grid template the radiologist to visualize the skin surface in the MR images and correlates each point in MRI image with the correspondence point on the actual grid template. An MRI-compatible handheld positioning device allows the radiologist to adjust the orientation of the needle in two directions. The targeting study average targeting error was 2.20 mm with a standard deviation of 0.69 mm.
Coffee Break 3:00 PM - 3:30 PM
Ultrasound Imaging and Image Guidance: Joint Session with Conferences 11315 and 11319
Location: Salon C
Haichong Kai Zhang, Worcester Polytechnic Institute (United States) ;
Efficient target tracking for 3D ultrasound guided needle steering
Author(s): Guillaume Lapouge, Techniques de l'Ingénierie Médicale et de la Complexité-Info Mathématiques et Applications, Grenoble (France), Lab. d'Informatique de Robotique et de Microelectronique de Montpellier, Ctr. National de la Recherche Scientifique (France), Univ. de Montpellier (France); Jocelyne Troccaz, Univ. Grenoble Alpes (France), Techniques de l'Ingénierie Médicale et de la Complexité-Info Mathématiques et Applications, Grenoble (France), Grenoble INP (France); Philippe Poignet, Univ. de Montpellier (France), Lab. d'Informatique de Robotique et de Microelectronique de Montpellier, Ctr. National de la Recherche Scientifique (France)
3D ultrasound imaging can be used in the context of robotic needle steering to reach a physical target with a flexible, steerable needle. During the insertion, the tissue may be deformed, thus requiring to track intra-operatively the target's displacement. This paper proposes a new approach for computationally inexpensive and precise tracking of a moving target in 3D B-mode ultrasound volumes. It interconnects intensity based tracking and motion estimation algorithms. An experimental validation on ex-vivo tissue is proposed with promising tracking precision (estimated average error of 0.3mm) while significantly lowering the computational cost when compared to classical block matching based tracking.
Multi-needle detection in 3D ultrasound images using sparse dictionary learning
Author(s): Yupei Zhang, Xiuxiu He, Zhen Tian, Jiwoong Jeong, Yang Lei, Tonghe Wang, Qiulan Zeng, Ashesh B. Jani, Walter J. Curran, Pretesh Patel, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
Automatic brain structure-guided registration of pre and intra-operative 3D ultrasound for neurosurgery
Author(s): Soumya Ghose, David M. Mills, Jhimli Mitra, L. Scott Smith, Desmond T. B. Yeo, GE Global Research (United States); Alexandra J. Golby, Sarah F. Frisken, Brigham and Women's Hospital (United States); Thomas F. Foo, GE Global Research (United States)
Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. In this work, deformable registration of segmented brain structures in pre- and intraoperative 3D ultrasound was used to register pre- and intraoperative ultrasound. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 patients pre- and intraoperative ultrasound images of a public dataset.
A complex dual-modality kidney phantom for renal biopsy studies
Author(s): Jose Vargas, Phuc Le, Maysam Shahedi, Sarah Shahub, The Univ. of Texas at Dallas (United States); Jeffrey Gahan, Brett Johnson, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Matthew Pfefferle, Blake O. Judson, Yasmeen Alshara, The Univ. of Texas at Dallas (United States); Qinmei Li, The Univ. of Texas at Dallas (United States), The Second Affiliated Hospital of Guangzhou Medical Univ. (China); James D. Dormer, The Univ. of Texas at Dallas (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
In this paper, the goal was to develop a reliable and repeatable process to create realistic, kidney phantoms with tunable image visibility under ultrasound and CT imaging modalities for renal biopsy evaluation. The final complex kidney phantom was devised containing kidney cortex, medulla, and ureter. Simultaneously, some lesions were integrated into the phantom to mimic the presence of tumors during a biopsy. The phantoms were created and scanned by ultrasound and CT scanners and the result was a successful advancement in knowledge of materials with acoustic and impedance properties to replicate human organs for the field of image-guided interventions.
Automatic needle localization in intraoperative 3D transvaginal ultrasound images for high-dose-rate interstitial gynecologic brachytherapy
Author(s): Jessica R. Rodgers, Derek J. Gillies, Robarts Research Institute, Western Univ. (Canada); William T. Hrinivich, Johns Hopkins Univ. (United States); Igor Gyackov, Aaron Fenster, Robarts Research Institute, Western Univ. (Canada)
Gynecologic interstitial brachytherapy requires multiple needles to be precisely inserted into the tumor to deliver optimal treatment and avoid nearby healthy organs. We propose a 360° 3D transvaginal ultrasound (TVUS) guidance system for visualization of needles and report on the implementation of two automatic needle segmentation algorithms to aid the localization of needles intraoperatively, enhancing the clinical utility and translation of this system. A convolutional neural network approach and a randomized 3D Hough transform-based method were developed and tested for intraoperative 2D and 3D TVUS images, respectively, allowing for immediate adjustments to needle trajectories and potentially improving implant quality.
Ring-arrayed forward-viewing ultrasound imaging system: a feasibility study
Author(s): Ryosuke Tsumura, Doua P. Vang, Worcester Polytechnic Institute (United States); Nobuhiko Hata, Brigham and Women's Hospital (United States); Haichong Zhang, Worcester Polytechnic Institute (United States)
We proposed a novel data acquisition method with a ring-arrayed transducer to enable forward-viewing US imaging for being compatible with the guidance of needle insertion. The workflow of ultrasound (US)-guided biopsy requires a physician to hold the US probe to locate the interesting area with the non-dominant hand because the dominant hand is occupied by the biopsy needle. Although 3D US imaging is an emerging alternative that provides geometrical information better and used in the needle intervention, it is not enough to perform the low-cost imaging and the co-registered guidance for the needle intervention. Therefore, there is a demand for a solution providing simple/intuitive and accurate guidance and localization of the needle to elevate the conventional US-guided biopsy.
Monday 17 February Show All Abstracts
Image-guided Neurosurgical Interventions
Ivo Wolf, Hochschule Mannheim (Germany) ;
Comparison of head pose tracking methods for mixed-reality neuronavigation for transcranial magnetic stimulation
Author(s): Supriya Sathyanarayana, Christoph Leuze, Brian Hargreaves, Bruce L. Daniel, Jennifer A. McNab, Stanford Univ. (United States)
Repetitive Transcranial Magnetic Stimulation (rTMS) requires an electromagnetic coil to be accurately targeted to stimulate specific brain areas. A mixed-reality neuronavigation system relies on accurate head tracking. The purpose of this study is to compare three low-cost head tracking methods for mixed-reality neuronavigation. We developed and measured the accuracy of three head tracking methods. All three methods achieved an accuracy < 5 mm for low head angles. For larger head rotations of 30°, the tracking accuracy of the marker-less method needs to be further improved to take advantage of its benefits over marker-based methods.
Localisation of the subthalamic nucleus in MRI via convolutional neural networks for deep brain stimulation planning
Author(s): John S. H. Baxter, Ehouarn Maguet, Pierre Jannin, Univ. de Rennes 1 (France)
Deep brain stimulation (DBS), a treatment for Parkinson's disease, involves the placement of electrodes targeting specific brain structures. Due to the small size of targets such as the subthalamic nucleus (STN), their localisation in pre-operative MRI is of great value. Often, this is atlas-based, which is either time-consuming or prone to error. This paper develops a framework for STN localisation in MRI using convolutional neural networks. Compared against two atlas-based segmentations, it decreases error by 0.45 +- 0.76 mm and 0.48 +- 0.76 mm while reducing computation time. This method allows us to automatically identify the STN for DBS planning.
Intraoperative thermographic perfusion mapping in neurosurgery using regularized semiparametric regression
Author(s): Juliane Müller, Universitätsklinikum Carl Gustav Carus Dresden, TU Dresden (Germany); Nico Hoffmann, Helmholtz-Zentrum Dresden-Rossendorf e. V. (Germany); Martin Oelschlägel, Christian Schnabel, Gerald Steiner, Edmund Koch, Stephan B. Sobottka, Gabriele Schackert, Universitätsklinikum Carl Gustav Carus Dresden, TU Dresden (Germany); Matthias Kirsch, Universitätsklinikum Carl Gustav Carus Dresden, TU Dresden (Germany), Asklepios Kliniken GmbH (Germany)
The success of brain surgery depends on the exact and effective treatment of pathological alterations while preserving functional tissue and essential vessels. Varying intraoperative imaging methods have been developed to achieve this goal. For cortical perfusion imaging, application of time-resolved thermography in combination with an intravenously applied cold bolus became a promising approach. This work provides a regularized semiparametric regression framework that detects the cold signal response function while compensating arbitrary background signals using penalized B-Splines. This enables weak thermal signal detection to give information about blood flow even in small vessels, applicable for intraoperative cortical blood flow mapping.
A guidance system for electrode placement in epilepsy cases
Author(s): Xiaoyao Fan, David W. Roberts, Keith D. Paulsen, Dartmouth College (United States)
Brain deformation compensation for deep brain lead placement surgery: a comparison of simulations driven by surface and deep brain sparse data
Author(s): Chen Li, Xiaoyao Fan, Joshua P. Aronson, Keith D. Paulsen, Dartmouth College (United States)
Accurate surgical placement of electrodes is essential to successful deep brain stimulation (DBS) for patients with neurodegenerative diseases such as Parkinson's disease and essential tremor. However, the accuracy of pre-operative images used for surgical planning and guidance is often degraded by brain shift during surgery. To predict such intraoperative target deviation due to brain shift, we have developed a finite-element biomechanical model which assimilates intraoperative sparse data and calculates a whole brain displacement field. An updated image can then be generated by deforming the pre-operative one. This study demonstrates the potential of our model to predict brain shift near surgical targets by assimilating deep brain sparse data that is available via intraoperative ultrasound imaging.
MD Anderson Invited Session: Advances in Image-Guided, Data-Driven Interventions
Kristy K. Brock, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States) ;
10:10 AM - 10:30 AM Dr. David Jaffray: The Impact of Data Science on Image Guided Cancer Therapy
10:30 AM - 10:50 AM Dr. Bruno Odisio: Improving Outcomes in Focal Liver Ablation with Image Guidance
10:50 AM - 11:10 AM Dr. Jeffrey Weinberg: Advanced Imaging for Image-Guided Brain Surgery
AI-based Methods for Tissue Classification: Diagnosis and Therapy Applications
Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States) ;
Kristy K. Brock, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study
Author(s): Laura Connolly, Amoon Jamzad, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Martin Kaufmann, Queen's Cancer Research Institute, Queen's Univ. (Canada); Rachel Rubino, Alireza Sedghi, Queen's Univ. (Canada); Tamas Ungi, Mark Asselin, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Scott Yam, John Rudan, Christopher Nicol, Queen's Univ. (Canada); Gabor Fichtinger, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Parvin Mousavi, Queen's Univ. (Canada)
The iKnife is a surgical tool designed to provide chemical feedback about a tumor resection cavity by analyzing electrosurgical vapors. We build and compare machine learning classifiers that can distinguish primary cancer from surrounding tissue at different stages of tumor progression. Two cohorts of mice were injected with a human breast adenocarcinoma cell line. At 4 and 6 weeks after cell injection, mice in each cohort were sacrificed and analyzed by taking iKnife burns of the tumors and surrounding tissues. A neural network classifier is compared to PCA/LDA classification. Both classifiers can successfully distinguish primary cancer from surrounding tissue.
Towards democratizing AI in MR-based prostate cancer diagnosis: 3.0 to 1.5 Tesla
Author(s): Andrew Grebenisan, Alireza Sedghi, Queen's Univ. (Canada); Robert Siemens, Kingston General Hospital (Canada); Jason Izard, Queen's Univ. (Canada); Alexandre Menard, Kingston General Hospital (Canada); Parvin Mousavi, Queen's Univ. (Canada)
Nearly 1 in 14 men will be diagnosed with prostate cancer (PCa) in their lifetimes. The gold standard for PCa detection is pathology analysis of biopsy cores taken using trans-rectal ultrasound, a procedure with 30-45% false negatives. Multi-parametric MRI is becoming part of the standard of care for PCa detection, while deep learning approaches that aid with this are on the rise. Most of the proposed solutions benefit from abundant high-quality data, limiting their translation to clinical settings. Close to 83% of clinical MRI systems in Canada are 1.5 T, where many centres may not have high patient volume. We present preliminary results using a deep learning framework built from publicly available 3.0 T MP-MRI data and re-purposed for 1.5 T clinical data. We achieve AUC of up to 0.76 and provide visualization of the most informative image regions for the deep models. Our approach allows local centres to build models from their own cases, taking advantage of external large data sets.
Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net
Author(s): François-Xavier Carton, Univ. Grenoble Alpes (France), Vanderbilt Univ. (United States); Matthieu Chabanas, Univ. Grenoble Alpes (France), Vanderbilt Univ. (United States); Bodil K. R. Munkvold, Norwegian Univ. of Science and Technology (Norway); Ingerid Reinertsen, SINTEF (Norway); Jack H. Noble, Vanderbilt Univ. (United States)
During brain tumor resection, intraoperative ultrasound (iUS) can assist deciding whether the resection is complete or tumor tissue remains. However, iUS images are difficult to interpret because of the noise and artifacts. In particular, tumor tissue is difficult to distinguish from healthy tissue. In this paper, we propose an automatic method to segment low grade brain tumors in iUS images using a 3D U-Net. We trained the network with twelve cases and tested it on five cases, on three folds. The obtained results are promising, with a median Dice score of 0.72 compare to ground truth segmentations.
Cristian A. Linte, Rochester Institute of Technology (United States) ;
Baowei Fei, The Univ. of Texas at Dallas (United States)
Healthcare in need of innovation: exponential technology and biomedical entrepreneurship as solution providers (Keynote Presentation)
Author(s): Michael Friebe, Otto-von-Guericke Univ. Magdeburg (Germany)
There are significant challenges in global healthcare delivery. Some countries have abundant services, but are stuck with a rather nimble and expensive system that focuses on incremental innovations. Other geographies still in need of basic tools and infrastructure require completely different, inexpensive, and with that more disruptive solutions. Future healthcare with a focus on prevention/early detection and pro-active therapy will need to employ exponential technologies (AI+Big Data, Sensor Technology, Synthetic Biology, Robotics, 3D Printing, ...) that will surely lead to significant changes in the way we experience and deliver healthcare. An empowered patient will play a more and more important role. The biomedical engineering education will need to be adjusted to these developments to include innovation-generation-, learning-, and life skills taught within an interdisciplinary environment focussing on inventing solutions around identified and verified unmet clinical needs.
Augmented Reality for Image-guided Therapy
Eric J. Seibel, Univ. of Washington (United States) ;
Augmented reality visualization of hyperspectral imaging classifications for image-guided brain tumor resection
Author(s): James Huang, Martin Halicek, Maysam Shahedi, Baowei Fei, The Univ. of Texas at Dallas (United States)
Augmented reality (AR) for image-guided surgery seeks to enable surgeons to see tissue segmentations in real time. Hyperspectral imaging (HSI) is a non-contact, optical imaging modality, which rapidly acquires hundreds of images at different wavelengths, and can be combined with machine-learning for tissue segmentation. We construct a brain tissue phantom with blood vessels, tumor, and gray matter. Our application virtually superimposes the HSI and segmentations of the brain tumor phantom in the scene using the HoloLens. The user can manipulate, resize, rotate, and view HSI channels using hand or voice controls. We present a fast and practical optical-based AR method for brain surgical guidance.
Accuracy study of Smartglasses/Smartphone AR systems for percutaneous needle interventions
Author(s): Reza Seifabadi, Ming Li, Dilara Long, Sheng Xu, Bradford J. Wood, National Institutes of Health Clinical Ctr. (United States)
This study aims to investigate the accuracy of a cross platform augmented reality (AR) system for percutaneous needle interventions irrespective of operator error. In particular, we study the effect of the relative position and orientation of the AR device and the marker, the location of the target, and the angle of needle on the overlay accuracy for different AR platforms.
Augmented reality-assisted biopsy of soft tissue lesions
Author(s): Patric Bettati, The Univ. of Texas at Dallas (United States); Majid Chalian, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); James Huang, James D. Dormer, Maysam Shahedi, The Univ. of Texas at Dallas (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Biopsy of small soft tissue lesions can be difficult in the presence of sensitive organs. While CT volumes can be acquired for lesion localization, patient position and motion prior to the start of the biopsy can make it difficult to translate the lesion location from the CT onto the patient body. We present an augmented reality-guided biopsy procedure for soft tissue lesions, quantified with a phantom study. We found an average error of 0.75 cm when AR guidance was used, compared to an error of 1.52 cm from the center of the lesion during unguided biopsy.
Towards augmented reality-based suturing in monocular laparoscopic training
Author(s): Chandrakanth Jayachandran Preetha, Otto-von-Guericke Univ. Magdeburg (Germany); Fabian Siegfried Wehrtmann, UniversitätsKlinikum Heidelberg (Germany); Lalith Sharan, Hochschule Mannheim (Germany); Carolyn Fan, UniversitätsKlinikum Heidelberg (Germany); Jonathan Kloss, Otto-von-Guericke Univ. Magdeburg (Germany); Beat Peter Müller-Stich, Nickel Felix, UniversitätsKlinikum Heidelberg (Germany); Sandy Engelhardt, Hochschule Mannheim (Germany)
Conventional endoscopic systems output monocular video which compromises depth perception, spatial orientation and field of view. Suturing is one of the most complex tasks performed under these circumstances. Reliable 3D localization of needle and instruments in real time could be used to augment the scene with additional parameters that describe their quantitative geometric relation, e.g. the relation between the estimated needle plane and its rotation center and the instrument. The paper proposes an Augmented Reality environment with quantitative and qualitative visual representations to enhance laparoscopic training outcomes performed on a silicone pad. This is enabled by a multi-task supervised deep neural network which performs multi-class segmentation and depth map prediction. Scarcity of labels has been conquered by creating a virtual environment which resembles the surgical training resembles the surgical training scenario to generate dense depth maps and segmentation maps.
Session Plen:
Location: Salon F
Session Chairs: Metin N. Gurcan, Wake Forest Baptist Medical Ctr. (United States) and Georgia D. Tourassi, Oak Ridge National Lab. (United States)
4:00 PM - 4:30 PM: Award presentations
Are today's Mixed Reality experience pillars and hardware architectures well aligned with the specific needs of medical imaging and surgical guidance? (Plenary Presentation)
Author(s): Bernard C. Kress, Microsoft Corp. (United States)
Mixed Reality (MR) headsets have the potential to revolutionize the way we work, learn, communicate, and get entertained. The main pillars for MR development are wearable, visual and social comfort, as well as immersion experience. Do these pillars intersect the specific needs of medical imaging and surgical guidance applications? We will review the various challenges to implement MR hardware specifically adapted to such tasks with today's start of the art MR headset technology.
Session PSMon:
Multi-destination procedure planning for comprehensive lymph node staging bronchoscopy
Author(s): Trevor K. Kuhlengel, William E. Higgins, The Pennsylvania State Univ. (United States)
Lung cancer is the deadliest form of cancer. Accurate lymph node staging is critical for the management of lung cancer. Physicians typically use minimally invasive bronchoscopy to perform lymph node staging procedures, which are greatly aided by image-guided bronchoscopy (IGB) systems. To date, IGB systems only manage individual diagnostic sites, while effective lymph node staging requires biopsying multiple stations across the central chest. We propose new methods tailored to planning a more comprehensive lymph node staging procedure, suitable for IGB-based guidance systems. Our development features two contributions. First, a method for defining the nodal staging zones and automatically labeling lymph nodes. Second, a method for creating efficient multi-destination guidance plans visiting diagnostic sites throughout the central chest. We demonstrate our methods using CT image data collected from human lung cancer patients.
Virtual radial-probe endobronchial ultrasound for image-guided bronchoscopy
Author(s): William E. Higgins, Wennan Zhao, Rebecca Bascom, Jennifer Toth, The Pennsylvania State Univ. (United States)
Image-guided bronchoscopy systems have been developed to enable better planning and navigation. Radial probe endobronchial ultrasound (RP-EBUS)-guided transbronchial needle aspiration (TBNA) has demonstrated its potential over conventional TBNA to improve the diagnostic yield of peripheral pulmonary nodule biopsy. Studies also show that the combination of RP-EBUS and image-guided bronchoscopy can improve the diagnostic yield. However, the image-guided bronchoscopy systems provide no guidance for RP-EBUS and biopsy targeting. We expand the concept of VB and build a virtual RP-EBUS model to simulate the RP-EBUS probe in the CT-based virtual chest space.
Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shif
Author(s): Nazim Haouchine, Brigham and Women's Hospital, Harvard Medical School (United States); Parikshit Juvekar, Alexandra J. Golby, William M. Wells, Brigham and Women's Hospital (United States); Stephane Cotin, Institut National de Recherche en Informatique et en Automatique (France); Sarah Frisken, Brigham and Women's Hospital (United States)
Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. We present a novel method to compensate for brain shift, through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagates cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance.
Rigid and deformable corrections in real-time using deep learning for prostate fusion biopsy
Author(s): Aditya Bhardwaj, SAMSUNG R&D Institute India, Bangalore (India); Jun-Sung Park, SAMSUNG Electronics Co., Ltd. (Korea, Republic of); Soumik Mukhopadhyay, Samsung R&D Institute India - Bangalore (India); Sikander Sharda, Self Employed (India); Yuri Son, SAMSUNG Electronics Co., Ltd. (Korea, Republic of); Bhavya Nishitkumar Ajani, Samsung R&D Institute India - Bangalore (India)
Fusion biopsy reduces false negative rates in prostatic cancer detection compare to systemic biopsy. However, accuracy in biopsy sampling depends upon quality of alignment between pre-operative 3D MR and intra-operative 2D US. During live biopsy, the US-MR alignment may be disturbed due to prostate or patient rigid motion. Further, prostate gland deform due to probe pressure, which add error in biopsy sampling. In this paper, we describe a method for real-time 2D-3D multi-modal registration, utilizing deep learning, to correct for rigid and deformable errors. Our method do not require an intermediate 3D US and works in real-time with an average runtime of 112 ms for both rigid and deformable corrections. On 12 patient data, our method reduces mean trans-registration error (TRE) from 8.890±5.106 mm to 2.988±1.513 mm, comparable to other state of the arts in accuracy.
Automated classification of brain tissue: comparison between hyperspectral imaging and diffuse reflectance spectroscopy
Author(s): Marco Lai, Philips Research (Netherlands); Simon Skyrman, Karolinska Univ. Hospital (Sweden); Caifeng Shan, Elvira Paulussen, Philips Research (Netherlands); Francesca Manni, Technische Univ. Eindhoven (Netherlands); Akash Swamy, Drazenko Babic, Philips Research (Netherlands); Erik Edström, Oscar Persson, Gustav Burström, Adrian Elmi Terander, Karolinska Univ. Hospital (Sweden); Benno H. W. Hendriks, Philips Research (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Malignant gliomas make up approximately 80% of all malignant brain tumors and they are primarily managed by surgical resection. The use of imaging technique to delineate tumor margins and distinguish several tissue types, would offer a valuable aid during surgery. In this study, the use of Diffuse Reflectance Spectroscopy (DRS) and Hyperspectral Imaging (HSI) for brain tissue and tumor classification has been investigated. The applied method for classification is based on Machine Learning. This feasibility study shows that HSI and DRS can potentially be used as non-invasive tumor detection methods and can contribute to the effectiveness of surgical oncology.
Automatic fiducial marker detection and localization in CT images: a combined approach
Author(s): Milovan Regodic, Zoltan R. Bardosi, Wolfgang Freysinger, Medizinische Univ. Innsbruck (Austria)
Automatizing fiducial detection and localization in pre-operative images of the patient can lead to better registration accuracy, reduced human errors, and intervention time. We present an algorithm that builds on earlier approaches with morphological functions and pose estimation algorithms. A Convolutional Neural Network (CNN) is proposed for the fiducial classification task. A digital experiment, with cone-beam CT (CBCT) imaging software, is performed to determine the accuracy of the algorithm. The achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) µm and 14 (6) µm, respectively.
Error analysis for a navigation system using 3D abdominal ultrasound
Author(s): David Iommi, Alejandra Valladares, Michael Figl, Johann Hummel, Medizinische Univ. Wien (Austria)
We present a systematic error analysis for a navigation system which allows image fusion between two arbitrary image modalities. A prototype was built for prostate biopsies merging pre-operative magnetic resonance images (MRI) with interventional 3D free-hand transrectal US (TRUS). The fusion between MRI and TRUS was performed by replacing the inter-modal MRI-TRUS registration by an intra-modal 3D/3D-US/TRUS registration and a pre-operative image fusion between MRI and 3D(-abdominal)-US. The target registration error between the MRI and 3D(-abdominal)-US was 1.81 mm. The Dice-score calculated from test lesions (radius 5 mm) segmented in MRI and TRUS resulted in an error of 0.67.
Flexible piezoelectric sensor for real-time image-guided colonoscopies: a solution to endoscopic looping challenges in clinic
Author(s): Adam Kenet, Eashwar Mahadevan, Sanjay Elangovan, Justin Yan, Kamran Siddiq, Simon Liu, Amrita Ladwa, Roshini Narayanan, Johns Hopkins Univ. (United States); Jessica Dakkak, Johns Hopkins Univ. (United States); Kenneth Ng, Amir Manbachi, Johns Hopkins Univ. (United States)
One common challenge in performing colonoscopies is endoscopic looping, which forms when the colonoscope bends in on itself within the colon. 79% of pain during colonoscopies results from looping. De-looping the colonoscope can add between 2-45 minutes to the duration of the procedure, depending on the experience of the endoscopist, since the exact location of the loop may not always be known. This study reports a low-cost endoscopy visualization device aimed to decrease and prevent looping occurrences. This is done through design, development, and validation of a flexible piezoelectric sensor capable of detecting forces applied during looping, bending, or compression.
Feasibility study of catheter segmentation in 3D Frustum ultrasounds by DCNN
Author(s): Lan Min, Hongxu Yang, Technische Univ. Eindhoven (Netherlands); Caifeng Shan, Alexander F. Kolen, Philips Research (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
3D ultrasound has been developed rapidly in medical intervention therapies, such as cardiac catheterization. Image-based catheter detection is studied to help sonographer to timely localize the instrument in the 3D US images. Conventionally, the 3D imaging methods are based on the Cartesian domain, which is limited by bandwidth and information lose when it is converted from the original acquisition space---Frustum domain. The catheter segmentation in the Frustum space helps to reduce the computational cost and improve efficiency. In this paper, we present a catheter segmentation method in 3D Frustum image via a deep convolutional network (DCNN). To accelerate the prediction efficiency on whole US Frustum volume, a filter-based pre-selection is applied to reduce the computational cost of the DCNN. Based on experiments on the ex-vivo dataset, our proposed method can segment the catheter in Frustum images with 0.67 Dice score within 3 seconds.
Blood flow anomaly detection via generative adversarial networks: a preliminary study
Author(s): Asha Singanamalli, Jhimli Mitra, Kirk Wallace, Prem Venugopal, L. Scott Smith, Larry Mo, GE Global Research (United States); Lai Yee Leung, Uniformed Services Univ. of the Health Sciences (United States); Jonathan Morrison, Univ. of Maryland, Baltimore (United States); Todd Rasmussen, Uniformed Services Univ. of the Health Sciences (United States); Luca Marinelli, GE Global Research (United States)
This work explores an unsupervised Generative Adversarial Network (GAN) based anomaly detection method for hemorrhage detection on color Doppler ultrasound images. As an initial, feasibility study, we collect and train the network on ultrasound color Doppler images of brachial arteries from healthy volunteers. On account of challenges in collecting hemorrhage data, we generate synthetic images of blood flow velocity patterns at the site of hemorrhage for testing. Results show significant differences in anomaly scores of normal arteries and simulated injured arteries. The residual images, or the reconstruction error maps, show promise in localizing anomalies at pixel level.
Exploiting confident information for weakly supervised prostate segmentation based on image-level labels
Author(s): Zhang Chen, Zhiqiang Tian, Xiaojian Li, Yinshu Zhang, Xi'an Jiaotong Univ. (China)
Workflow for creation and evaluation of virtual nephrolithotomy training models
Author(s): Catherine O. Wu, Kyle Sunderland, Queen's Univ. (Canada); Mihail Filippov, Ben Sainsbury, Marion Surgical (Canada); Gabor Fichtinger, Tamas Ungi, Queen's Univ. (Canada)
Percutaneous nephrolithotomy is the most common surgical treatment for the removal of kidney stones. This study proposes a workflow to generate 3D soft tissue and bone models from computed tomography (CT) images, to be used and validated in a VR nephrolithotomy simulator. The produced models were successfully integrated into Marion Surgical's haptic K181 PCNL and Kidney Access Array system, and validation tests were performed by experienced urological surgeons. The models were confirmed to be effective for haptic simulation while maintaining anatomical accuracy.
Value based decision support to prioritize development of innovative technologies for image-guided vascular surgery in the hybrid operating theater
Author(s): Friso G. Heslinga, Hendrik Koffijberg, Univ. of Twente (Netherlands); Robert H. Geelkerken, Univ. of Twente (Netherlands), Medisch Spectrum Twente (Netherlands); Robbert Meerwaldt, Medisch Spectrum Twente (Netherlands); Thijs G. ter Mors, GE Healthcare (Netherlands); Carine J. M. Doggen, J. Marjan Hummel, Univ. of Twente (Netherlands)
Innovative technologies for minimally invasive interventions have the potential to add value to image-guided vascular procedures in the hybrid operating theater (HOT). Restricted budgets require prioritization of the development of these technologies. We aim to provide vascular surgeons with a structured methodology to incorporate possibly conflicting criteria in prioritizing the development of new technologies. We propose a multi-criteria decision analysis framework to evaluate the value of innovative technologies for the HOT based on the MACBETH methodology. The framework is applied to a specific case: the new HOT in a large teaching hospital.
Open source software platform for interstitial ablation treatment planning
Author(s): Alice Santilli, Csaba Pinter, Lab. for Percutaneous Surgery (Canada); Bote Jiang, Queen's Univ. (Canada); Gabor Fichtinger, Lab. for Percutaneous Surgery (Canada); Gernot Kronreif, ACMIT GmbH (Austria)
We propose an open source software platform for the planning of image-guided interstitial ablation procedures, providing generic functionality and support for specialized plug-ins. The user segments the relevant structures, places fiducial points as ablation needle entries and tips, sets the ablation time, and the thermal dose is calculated by a dose engine. The thermal dose is then visualized using a combination of isodose lines and surfaces. Quantitative feedback is provided by dose volume histograms. Researchers can use the platform to create a wide range of specialized applications by adding plug-ins for various types of ablation methods, thermal models, and dose calculation engines. Existing extensions of the platform provide real-time navigation. From our usability study, the users found the visual information well presented and the platform intuitive to use. The users averaged 4.4 recalculation attempts before finding an optimal treatment where 100% of the tumor reached necrosis
Automatic segmentation of spinal ultrasound landmarks with U-net using multiple consecutive images for input
Author(s): Victoria Wu, Tamas Ungi, Kyle Sunderland, Grace Pigeau, Abigael Schonewille, Gabor Fichtinger, Queen's Univ. (Canada)
Scoliosis screening is currently only implemented in a few countries due to the lack of a safe and accurate measurement method. Spinal ultrasound is a viable alternative to X-ray, but manual annotation of images is difficult and time consuming. We propose using deep learning through a U-net neural network that takes consecutive images per individual input, as an enhancement over using single images as input for the neural network. To accommodate for consecutive input images, the ultrasound images were exported along with previous images stacked to serve as input for a modified U-net. Resulting output segmentations were evaluated based on the percentage of true negative and true positive pixel predictions.
Applications of VR medical image visualization to chordal length measurements for cardiac procedures
Author(s): Patrick Carnahan, Western Univ. (Canada), Robarts Research Institute (Canada); John T. Moore, Robarts Research Institute (Canada); Daniel Bainbridge, London Health Sciences Ctr. (Canada); Gavin Wheeler, Shujie Deng, King's College London (United Kingdom); Kuberan Pushparajah, King's College London (United Kingdom), Evelina Children's Hospital (United Kingdom); Elvis C. S. Chen, Robarts Research Institute (Canada), Western Univ. (Canada); John M. Simpson, King's College London (United Kingdom), Evelina Children's Hospital (United Kingdom); Terry M. Peters, Robarts Research Institute (Canada), Western Univ. (Canada)
Recently, developments have been made on improving 3D ultrasound (US) spatial compounding tailored for cardiac images. Compounded volumes are able to capture anatomical structures at a level similar to a CT scan, however these images are difficult to display and visualize due to an increased amount of surrounding tissue captured including excess noise at the volume boundaries. Traditional medical image manipulation software does not easily allow for viewing 2D slices at arbitrary angles, and 3D rendering techniques do not adequately capture depth information without the use of advanced transfer functions or other depth-encoding techniques that must be tuned to each individual data set. We demonstrate the novel use of a VR system for the application of measuring chordal lengths from compounded transesophageal and transgastric ultrasound images.
Stereovision-updated image guidance in multi-level open spine surgery: short vs. long exposure
Author(s): Xiaoyao Fan, Maxwell S. Durtschi, Chen Li, Dartmouth College (United States); Linton T. Evans, Dartmouth-Hitchcock Medical Ctr. (United States); Songbai Ji, Yunliang Cai, Worcester Polytechnic Institute (United States); Sohail K. Mirza, Keith D. Paulsen, Dartmouth College (United States)
Assessment of skill translation of intrathecal needle insertion using real-time needle tracking with an augmented reality display
Author(s): Saleh Choueib, Queens Univ. (Canada); Ciara McGarry, Lindsey Patterson, Melanie Jaeger, Tamas Ungi, Gabor Fichtinger, Queen's Univ. (Canada)
Current day lumbar puncture simulators lack a system for visual feedback. We propose the design of a work flow for lumbar puncture simulators that introduces a mechanism for visual feedback using a tracking system and augmented reality. We aim to assess the advantages of the stereoscopic capability of virtual reality (VR) on the transferability of skill for needle insertion. We augmented traditional lumbar puncture simulator workflow with visual feedback capabilities using VR and accurate tracking of needle relative to the spine model. Further, the visual feedback, provided in the VR medium, also allows for stereoscopic capabilities to offer more comprehensive spatial awareness of the target anatomy.
Multi-slot extended view imaging on the O-Arm: image quality and application to intraoperative assessment of spinal morphology
Author(s): Xiaoxuan Zhang, Ali Uneri, Michael D. Ketcha, Sophia A. Doerr, Johns Hopkins Univ. (United States); Patrick A. Helm, Medtronic, Inc. (United States); Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
A new O-arm configuration that incorporates a multi-slot collimator for extended view (EV) imaging was investigated in terms of image quality and application to intraoperative evaluation of spinal alignment. Image quality with respect to depth resolution and geometric accuracy was evaluated in comparison to 2D fluoroscopy. A cadaver study was conducted to further evaluate the feasibility and accuracy of the EV image for measurement of global spinal alignment. EV images showed a modest degree of tomosynthesis-like depth resolution and a high degree of spatial accuracy (<1 mm longitudinal distortion), demonstrating reliable measurement of spinal morphology over long lengths.
Computer vision-guided bronchoscopic navigation using dual CNN-generated depth images and ICP registration
Author(s): Xinqi Liu, Brigham and Women's Hospital (United States), Concord Academy (United States); Jonah R. Berg, Brigham and Women's Hospital (United States), Rivers School (United States); Franklin King, Nobuhiko Hata, Brigham and Women's Hospital (United States)
Automated medical image based anatomical 3D model generation for augmented reality applications
Author(s): Raveen S. Kariyawasam, Josiah E. Somani, Olivia Sorci, Hiromichi Nagayumi, Charles E. Witmer, Brian J. Park, Chamith S. Rajapakse, Univ. of Pennsylvania (United States)
The traditional workflow of 3D model generation for Augmented Reality involves manual processes that require extensive time and training. Our workflow addresses these issues by automating key procedures. This study evaluates the efficacy of our automated workflow by comparing with the manual workflow, its efficiency and quality in generating the models. The time to generate 3D structures from bone and skin CT-scans were measured. Additionally, the final models' discrepancies were marked. The automated workflow produced smoother, detailed models at a faster rate. The application of our methodology in surgical preparation may reduce risks through the convenience of medical image visualization.
Patient-specific, dynamic models of the tricuspid valve for simulation and planning
Author(s): Nora Boone, Robarts Research Institute (Canada); Hannah H. Nam, Matthew A. Jolley, The Children's Hospital of Philadelphia (United States); John Moore, Terry Peters, Olivia Ginty, Patrick Carnahan, Robarts Research Institute (Canada); Christian Herz, The Children's Hospital of Philadelphia (United States); Andras Lasso, Queen's Univ. (Canada)
Children with hypoplastic left heart syndrome (HLHS) rely on a single right ventricle and associated tricuspid valve. Tricuspid regurgitation is associated with heart failure and death. Understanding and modeling mechanisms of dysfunction, as well as the ability to test repairs, is critical to advancing tricuspid valve repair in this vulnerable population. We demonstrate the feasibility of 3D echocardiogram derived, patient specific TV models for characterization of the TV, and the exploration the relationship between specific anatomical features and TR severity. The simulations may be relevant presurgical planning of particularly complex and unique anatomies presented in children with HLHS.
A Windows GUI application for real-time image guidance during motion-managed proton beam therapy
Author(s): Zheng Zhang, Chris Beltran, Stephen M. Corner, Amanda J. Deisher, Michael G. Herman, Jon J. Kruse, Hok Seum Wan Chan Tseung, Erik J. Tryggestad, Mayo Clinic (United States)
Respiratory motion-management is particularly important for spot-scanned proton therapy, where motion-induced dosimetric "interplay" effects can severely perturb dose distribution. Our vendor developed a stereoscopic kV X-ray image-guidance platform. However, this vendor-provided solution lacks the capability to handle X-ray fluoroscopic imaging, which is crucial for aspects of motion management. To address this clinical gap, we developed a Windows-based software tool in .NET/C# to passively acquire the vendor's flat-panel detector (FPD) data stream (CameraLink) and handle fluoroscopic images in real time. This therapy tool automatically archives clinical X-ray data, and serves as a platform for image guidance research in the future.
Automated segmentation of computed tomography colonography images using a 3D U-Net
Author(s): Keiran Barr, Jacob Laframboise, Tamas Ungi, Lab. for Percutaneous Surgery (Canada); Lawrence Hookey, Queen's Univ. (Canada); Gabor Fichtinger, Lab. for Percutaneous Surgery (Canada)
The segmentation of computed tomograpy (CT) colonography images is important to both colorectal research and diagnosis. This process currently relies on manual interaction, so there is unavoidable interrater variability. This study proposes an automated approach to segmentation using an implementation of U-Net. This research is conducted on 76 CT scans. K-fold cross-validation is applied, and accuracy assessed by the Sørensen-Dice coefficient. Average network accuracy is 98.81%, and standard deviation of K accuracies is 0.5%. The network performs with considerable accuracy and consistency. This method for automatic segmentation could serve a supplemental or alternative tool for threshold-based segmentation.
Spherical harmonics for modeling shape transformations of breasts following breast surgery
Author(s): Urmila Sampathkumar, Zhale Nowroozilarki, Univ. of Houston (United States); Gregory P. Reece, Summer E. Hanson, The Univ. of Texas M. D. Anderson Cancer Ctr. (United States); Fatima A. Merchant, Univ. of Houston (United States)
Breast shape aesthetic is the most desired outcome of cosmetic and reconstructive breast surgery. However, the lack of modeling tools to generate realistic simulations of post-operative results makes communication of surgical outcomes to patients challenging. We propose a shape transformation model that uses three-dimensional surface images of the breasts in conjunction with spherical harmonics modeling to transform the shape of a pre-operative breast to its post-operative shape for a specific breast surgery. The proposed simulation approach shows promising results and will provide additional decision-making tools that will benefit patients undergoing breast surgery.
Deep learning-based automatic prostate segmentation in 3D transrectal ultrasound images from multiple acquisition geometries and systems
Author(s): Nathan Orlando, Derek J. Gillies, Igor Gyackov, Aaron Fenster, Robarts Research Institute (Canada)
Minimally invasive procedures like MRI/3D transrectal ultrasound (TRUS) fusion-guided prostate biopsy and high-dose-rate prostate brachytherapy (HDR-BT) rely on accurate segmentation of the prostate; however, manual prostate segmentation in 3D TRUS is time-consuming and difficult. We propose a deep learning approach to segment the prostate in 3D TRUS images from multiple acquisition methods and systems to reduce segmentation error and procedure time. The 3D segmentation approach involved prediction on 2D radial slices and reconstruction into a 3D geometry. Our algorithm performed with high accuracy (DSC=92.2%) and fast computation time (<0.7s) on a diverse side-fire 3D TRUS dataset, demonstrating potential generalizability.
Dual wavelength silica fiber compatible image-guided laser brain cancer surgery
Author(s): Nitesh Katta, Arnold D. Estrada, Austin McElroy, Thomas E. Milner, The Univ. of Texas at Austin (United States)
Surgical oncology can be benefit from specialized tools that enhance diagnostic imaging, enable precise cutting and removal of tissue without damage to adjacent structures. Particularly in the brain, higher precision surgical devices are needed for tumor resections near critical neural structures. We demonstrate feasibility of an image-guided co-aligned laser surgical system for excision of brain tumors in vivo in a murine xenograft model. The system combines optical coherence tomography (OCT) guidance with surgical lasers for high-precision tumor ablation and microcirculation coagulation. We demonstrate the system for accurate imaging of tumor vasculature and margins without exogenous contrast agents in a mouse xenograft cancer model. Tumor sizing and margin detection qualitatively correlated to dimensions from histology. OCT image guidance enables precise tumor and tissue resection with a thulium (Tm) surgical laser.
Assessment of therapy applicator targeting with a mechanically assisted 3D ultrasound system for minimally invasive focal liver tumor therapy
Author(s): Derek J. Gillies, Robarts Research Institute (Canada), Western Univ. (Canada); Jeffrey Bax, Kevin Barker, Lori Gardi, David Tessier, Robarts Research Institute (Canada); Nirmal Kakani, Manchester Royal Infirmary (United Kingdom); Aaron Fenster, Robarts Research Institute (Canada), Western Univ. (Canada)
Minimally invasive focal ablation of liver cancer is an alternative technique to conventional methods for early-stage tumors. Sufficient therapy is provided when ablation applicators are placed at their intended target locations, but current practices are occasionally unable to achieve the required degree of accuracy. We have developed a mechanically assisted 3D ultrasound imaging and guidance system capable of providing geometrically variable images to increase intraoperative spatial information and propose a new method for placing therapeutic applicators during focal liver tumor ablations. End-to-end testing was performed with a mock phantom procedure to assess overall accuracy and a potential clinical workflow.
Video-based automatic endoscopic sinus surgery skill assessment with deep convolutional neural network
Author(s): Shan Lin, Univ. of Washington (United States); Fangbo Qin, Institute of Automation (China); Xinyu Gu, Jichun Li, Randall Bly, Kris Moe, Blake Hannaford, Univ. of Washington (United States)
Current surgical skill assessment process is tedious and subjective because it usually relies on evaluations by senior surgeons. The advent of new surgical techniques which require more intensive training motivates increasing requirements of efficient objective skill assessment methods. This study develops an automatic surgical skill assessment system based on the surgical instrument tip trajectories extracted from the endoscopic cadaveric trans-nasal sinus surgery videos. We proposed a tracking algorithm by combining deep convolutional neural network based segmentation and dense optical flow. We analyzed various conventional and new motion metrics extracted from the instrument tip trajectories. The proposed method has been tested with 15 endoscopic sinus surgery videos and shown its potential for the automatic surgical skill assessment.
Multi-slot intraoperative imaging and 3D-2D registration for evaluation of long surgical constructs in spine surgery
Author(s): Ali Uneri, Xiaoxuan Zhang, Michael D. Ketcha, Sophia A. Doerr, Johns Hopkins Univ. (United States); Patrick A. Helm, Medtronic, Inc. (United States); Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
A method for 3D registration and pose estimation of long surgical constructs is presented using a novel, intraoperative multi-slot radiography system on the O-arm. The approach leverages parallax views from the multi-slot collimator for 3D-2D registration of implants (pedicle screws) to raw detector measurements. Experiments conducted in phantom and cadaver demonstrated registration accuracy <1.5 mm, comparable to that achieved with dual-view (AP+LAT) radiography. The ability to localize instrumentation in long spinal constructs in the OR could improve guidance and quality assurance in multi-level spinal fusion and correction of spinal deformity.
Clinical context-based image registration methods for 3D image guidance during minimally invasive interventional procedures
Author(s): Ponraj Chinnadurai, Gouthami Chintalapani, Siemens Medical Solutions USA, Inc. (United States)
Real-time integration of information from pre-operative 3D or 4D imaging modalities (MRA or CTA) with intra-operative imaging modalities such as 2D fluoroscopy can augment minimally invasive interventional procedures by providing real-time 3D image guidance.Despite a plethora of image registration algorithms, the heterogeneity of complex clinical problems and missing clinical context during image registration process have resulted in lack of automation; thereby limiting the routine clinical adoption of these image registration techniques. In this paper, we present a novel concept of "PROCEDURAL-CONTEXT"-based image registration methods and describe a series of image features and their procedural context during image registration process for interventional procedures using real-world interventional case examples. Preliminary results from an aortic phantom validation experiment comparing spine versus vascular registration were also reported.
Development of ultrasonography assistance robot for prenatal care
Author(s): Ryosuke Tsumura, Worcester Polytechnic Institute (United States), Waseda Univ. (Japan); Hiroyasu Iwata, Waseda Univ. (Japan)
We proposed an ultrasonography assistance robot for prenatal care to improve the workload of obstetricians and gynecologists. Although several robotic-assisted ultrasound imaging platforms have been developed, there were few platforms focusing on prenatal care. In prenatal care, it is crucially important to satisfy the safety for the pregnant women and fetus compared to other regions of ultrasonography. The proposed robot system enabled to scan the entire uterus area under keeping the minimum contact force to the abdomen.
Data-driven detection and registration of spine surgery instrumentation in intraoperative images
Author(s): Sophia A. Doerr, Ali Uneri, Yixuan Huang, Xiaoxuan Zhang, Johns Hopkins Univ. (United States); Patrick Helm, Medtronic, Inc. (United States); Nick Theodore, Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
A method to detect and localize pedicle screws in intraoperative radiographs is presented, using a neural network to initialize model-based 3D-2D registration. The approach is especially useful to the "KC-Reg" approach that leverages known implant design, providing input on the number and orientation of screws and helping to avoid local minima and speed runtime. A U-Net architecture was trained on a clinical dataset with simulated instrumentation to segment, count, localize, and classify pedicle screws. Performance demonstrated segmentation accuracy >93% and pose detection within 2 mm and 4°, sufficient for the KC-Reg registration algorithm in guidance and verification of spine surgery.
Force and torque feedback in endoscopic vessel harvesting
Author(s): Julia Wiercigroch, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Keyvan Hashtrudi-Zaad, Queen's Univ. (Canada); Tamas Ungi, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Gianluigi Bisleri, Queen's Univ. (Canada); Gabor Fichtinger, Lab. for Percutaneous Surgery, Queen's Univ. (Canada)
Endoscopic vessel harvesting is the preferred minimally invasive approach to obtain grafts for coronary bypass surgeries but requires extensive practice to minimize vessel damage. We propose to create a surgical training simulation with visual and haptic feedback. In this study, we focus on measuring the forces and torques on the surgical retractor. The original retractor handle was modified to attach an ATI Mini40 force-torque transducer. The forces and torques in two radial artery and two saphenous vein procedures in human cadavers were recorded. The distribution of force and torque peaks in the retractor was found for endoscopic vessel harvests.
Multi-step segmentation for prostate MR image based on reinforcement learning
Author(s): Xiangyu Si, Zhiqiang Tian, Xiaojian Li, Zhang Chen, Gen Li, Xi'an Jiaotong Univ. (China)
Image-based extraction of breathing signal from cone-beam CT projections
Author(s): Shafiya Sabah, Salam Dhou, American Univ. of Sharjah (United Arab Emirates)
Lung cancer continues to be the most common type of cancer worldwide. In radiation therapy, high doses of radiation are used to kill tumors. Adapting radiotherapy to breathing patterns has always been a major concern for thoracic and upper abdomen tumors. The main objective of this work is to develop a method to extract breathing signal directly from a given sequence of cone-beam computed tomography (CBCT) projections. The proposed method implements optical flow to track the movement of pixels between each pair of successive CBCT projection images through the entire set of projections. Dimensionality reduction using Principal Component Analysis (PCA) are then applied on the optical flow dataset to reduce its dimensionality keeping only the most distinctive components which represent the breathing signal. The method was tested on XCAT phantom datasets simulating cases of regular and irregular breathing patterns. In all cases the extracted signal correlated well with ground truth signal.
A standardized method for accuracy study of MRI-compatible robots: case study- a body-mounted robot
Author(s): Eleni Siampli, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Health System (United States)
Here we report on a phantom targeting study for accuracy evaluation of our body-mounted robot for MR-guided arthrography using a standardized method developed in a multi-institute effort with the aim of providing an objective method for accuracy and signal-to-noise Ratio (SNR) evaluation of MRI-compatible robots. The targeting study was conducted using the quality assessment mockup phantom and associated software called QARAI that was developed by the URobotics Laboratory at Johns Hopkins and colleagues. The mockup contains four embedded fiducials and an 8 by 8 grid which is used to automatically identify the targeting points with high accuracy. The study was conducted on a Philips Achieva 1.5T MRI system and 10 points have been targeted. All targets were reached with an average error of 2.71mm. The targeting algorithm, as well as the control of the robot, were completed using robot control modules developed with the open source software 3D Slicer
Preoperative prediction of insertion depth of lateral wall cochlear implant electrode arrays
Author(s): Mohammad Mahmudur Rahman Khan, Robert F. Labadie, Jack H. Noble, Vanderbilt Univ. (United States)
Cochlear implants (CI) use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons towards more optimal placement of the array to reduce trauma and preserve the residual hearing. After analyzing the CT images of 86 lateral wall positioned straight electrode array CI recipients, a nonlinear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and base insertion depth (BID). The model is able to accurately predict angular insertion depths with standard deviation of 41 degrees.
Cochlear implant electrode sequence optimization using patient specific neural stimulation models
Author(s): Ziteng Liu, Ahmet Cakir, Jack H. Noble, Vanderbilt Univ. (United States)
Cochlear implants (CIs) use an array of electrodes implanted in the cochlea to directly stimulate the auditory nerve. After surgery, CI processor settings, including the firing order of the electrodes, need to be determined by an audiologist to optimize performance. We propose a new method to determine a customized firing order using image-based models of patient specific neural stimulation patterns. Our models permit estimating when deleterious overlapping stimulation between electrodes occurs. We propose and evaluate an automatic optimization algorithm to determine a custom firing order that minimizes the negative effects of the model-estimated stimulation overlap.
Renal biopsy under augmented reality guidance
Author(s): Matthew Pfefferle, Sarah Shahub, Maysam Shahedi, The Univ. of Texas at Dallas (United States); Jeffrey Gahan, Brett Johnson, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Phuc Le, Jose Vargas, Blake O. Judson, Yasmeen Alshara, Qinmei Li, The Univ. of Texas at Dallas (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Kidney biopsies are performed using preoperative CT imaging to identify the lesion of interest and intraoperative ultrasound imaging to guide the needle during the biopsy. Physicians need to perform a mental cross-modality fusion of the preoperative and intraoperative scans that limits the procedure accuracy and reproducibility. We developed an augmented reality system to display the lesion hologram superimposed on a phantom. This system allowed integration of preoperative CT scans with intraoperative ultrasound scans using automated image registration and a magnetic tracking system. Our method achieved a targeting accuracy of 2.9 mm in a renal phantom study.
Development of a novel tumor phantom model for head and neck squamous cell carcinoma and its applications
Author(s): Michael T. Sramek, Geisel School of Medicine (United States); Yuan Shi, Thayer School of Engineering at Dartmouth (United States); Erick A. Quintanilla, Dartmouth-Hitchcock Medical Ctr. (United States); Aravind S. Ponukumati, Geisel School of Medicine (United States); Xiaotian Wu, Thayer School of Engineering at Dartmouth (United States); David A. Pastel, Dartmouth-Hitchcock Medical Ctr. (United States); Ryan J. Halter, Thayer School of Engineering at Dartmouth (United States); Joseph A. Paydarfar, Dartmouth-Hitchcock Medical Ctr. (United States)
Head and neck surgical oncology is observing an increased incidence in oropharyngeal malignancy. Tissue phantoms (TP) are used in training and research throughout the surgical fields. For the development of a TP to be considered successful, it must mimic the malignancy in question radiographically, physically, and visually. Here, we develop a novel TP to mimic head and neck squamous cell carcinoma (HNSCC) with a novel 3D-printed syringe adaptor and discuss applications of the newly developed TP towards training of Otolaryngology residents and the development and validation of image-guidance technologies.
Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture
Paper 11315-100
Author(s): Roshan Reddy Upendra, Shusil Dangi, Cristian A. Linte, Rochester Institute of Technology (United States)
Cine cardiac magnetic resonance imaging (CMRI), the current gold standard for cardiac function analysis, provides images with high spatio-temporal resolution. Computing clinical cardiac parameters from these high resolution images is an importantstep in cardiac disease diagnosis, therapy planning and monitoring cardiac health. An accurate segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool is crucial for computing these clinical cardiac parameters. U-Net inspired models are the current state-of-the-art for medical image segmentation. SegAN, a novel adversarial network architecture with multi-scale loss function, has shown superior segmentation performance over U-Net models with single-scale loss function. In this paper, we compare the performance of stand-alone U-Net models and U-Net models in SegAN framework for segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool from the 2017 ACDC segmentation challenge dataset.
Optical imaging of dental plaque pH which can be used for predicting locations of tooth decay, guiding medicinal therapies, and monitoring enamel remineralization
Author(s): Manuja Sharma, Lauren K. Lee, Matthew D. Carson, Mark E. Fauver, Eric J. Seibel, Univ. of Washington (United States)
Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers
Author(s): Siddhartha Nanda, Jacob Antunes, Kaustav Bera, Case Western Reserve Univ. (United States); Justin Brady, Kenneth Friedman, Joseph Willis, Rajmohan Paspulati, Univ. Hospitals Cleveland Medical Ctr. (United States); Satish E. Viswanath, Case Western Reserve Univ. (United States)
In this study, we evaluated texture kinetic features of rectal tumors using baseline DCE MRI scans in order to predict pathologic tumor stage regression in response to neoadjuvant chemoradiation. Our texture kinetics approach utilized a combination of radiomic texture features (from multiple DCE uptake phases) and polynomial curve fitting to uniquely quantify spatiotemporal patterns of lesion texture during contrast uptake and diffusion that were different between responders and non-responders to nCRT. Texture kinetics of gradient XY enhancement yielded the best overall AUC=0.81, which was markedly higher than any feature enhancement representation (best AUC=0.72); indicating they may hold significant promise for characterizing treatment response on DCE MRI for rectal cancers.
A comprehensive workflow and framework for immersive Virtual Endoscopy of dissected aortae from CTA data
Author(s): Jan Egger, Technische Univ Graz (Austria); Simon Gunacker, Antonio Pepe, Gian Marco Melito, Christina Gsaxner, Jianning Li, Katrin Ellermann, Technische Univ. Graz (Austria); Xiaojun Chen, Shanghai Jiao Tong Univ. (China)
When the intima, i.e. the innermost layer of the aortic wall, tears, blood enters and propagates between the layers causing them to separate. This is known as aortic dissection (AD). Without immediate treatment, an AD may kill 33% within the first 24 hours, 50% within 48 hours, and 75% within 2 weeks. However, proper treatment is still subject to research. By providing a deeper understanding of aortic dissections, this work aims to contribute to the continuous improvement of AD diagnosis and treatment by presenting AD in an immersive visual experience: Virtual Reality. Given an AD scan, relevant visual information is segmented, refined and put into a 3D scene, and further enhanced by blood flow simulation and VR user interaction. The implementation serves as a prototype and is considered to be extended by minimizing user interaction when new CT scans are loaded into VR (i) and by providing an interface to feed the visualization with simulation data provided by mathematical models (ii).
A personalized approach for microwave ablation treatment planning fusing radiomics and bioheat transfer modeling
Author(s): Pinyo Taeprasartsit, Silpakorn Univ. (Thailand); Henky Wibowo, phenoMapper, LLC (United States); Jan Sebek, Punit Prakash, Kansas State Univ. (United States)
This study explores the potential of predicting an optimal dose for treating lung cancer using microwave ablation and identifying microwave sensitive radiomics features to predict individualized microwave dose. Shape, size, and texture data computed through 3D wavelets are employed as radiomics features for dose prediction. The proposed method obtained these features in three stages: (1) candidate-tumor detection (2) segmentation, and (3) radiomics feature extraction. Clinical data on microwave-ablation dose and follow-up outcomes were considered as validation data for dose prediction from a machine.
Obtaining the potential number of object models/atlases needed in medical image analysis
Author(s): Ze Jin, Tokyo Institute of Technology (Japan); Jayaram K. Udupa, Drew A. Torigian, Penn Medicine (United States)
We present a method for obtaining an optimum number of models/ atlases needed for capturing variations for certain given object. On a set of 298 head and neck CT data sets, our preliminary results show that 18 groups for mandible, 12 groups for esophagus, 8 groups for right parotid gland, 11 groups for cervical spinal cord, and 8 groups for right submandibular gland are needed based on our proposed method. We believe that these findings will also be helpful in any effort that requires collecting prior information such as in deep learning and other machine learning strategies.
Tuesday 18 February Show All Abstracts
Session 10:
Novel Imaging Technologies for Interventional Guidance
David R. Holmes, Mayo Clinic (United States) ;
Patient-specific deep deformation models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions
Author(s): Jhimli Mitra, Michael MacDonald, David Mills, Soumya Ghose, L. Scott Smith, Shourya Sarcar, Desmond T. B. Yeo, GE Global Research (United States); Clare M. C. Tempany, Brigham and Women's Hospital (United States); Bryan Bednarz, Sydney A. Jupitz, Univ. of Wisconsin-Madison (United States); Thomas K. F. Foo, GE Global Research (United States)
Image fusion-guided interventions often require planning MR/CT and interventional U/S images to be registered in real-time. In this work, we propose a novel framework to align planning and interventional 3D U/S by training patient-specific deep-deformation models (PsDDM) at the planning stage. Planning 3D U/S volumes are then efficiently warped onto the interventional 3D U/S volumes using the trained PsDDM, thus enabling the transfer of MR/CT information in real-time. The accuracy of registering using a trained PsDDM is shown using 8 liver U/S volumes with mean target registration error (TRE), 0.84mm ± 0.39mm for the centroid of a major vessel bifurcation.
Image guided mitral valve replacement: registration of 3D ultrasound and 2D x-ray images
Author(s): James D. Dormer, Md Fiaz Islam Bhuiyan, The Univ. of Texas at Dallas (United States); Nahian Rahman, Nancy Deaton, Georgia Institute of Technology (United States); Jun Sheng, Univ. of California, Riverside (United States); Muralidhar Padala, Emory Univ. (United States); Jaydev P. Desai, Georgia Institute of Technology (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Mitral valve repair or replacement is important in the treatment of mitral regurgitation. For valve replacement, a transcatheter approach had the possibility of decrease the invasiveness of the procedure while retaining the benefit of replacement over repair. However, fluoroscopy images acquired during the procedure provide no anatomical information regarding the placement of the probe tip once the catheter has entered a cardiac chamber. By using 3D ultrasound and registering the 3D ultrasound images to the fluoroscopy images, a physician can gain a greater understanding of the mitral valve region during transcatheter mitral valve replacement surgery.
Multi-view 3D echocardiography volume compounding for mitral valve procedure planning
Author(s): Patrick Carnahan, Western Univ. (Canada), Robarts Research Institute (Canada); John T. Moore, Robarts Research Institute (Canada); Daniel Bainbridge, London Health Sciences Ctr. (Canada); Elvis C. S. Chen, Terry M. Peters, Robarts Research Institute (Canada), Western Univ. (Canada)
For procedures targeting the mitral valve, transesophageal echocardiography (TEE) is the primary imaging method used as it provides clear 3D images of the valve and surrounding tissue. However, TEE has limitations in signal dropout and artefacts, particularly for structures lying below the valve. In order to see these structures, alternative echo views are required, however the entire ventricle cannot be directly visualized in sufficient detail from a single image acquisition in 3D. We propose applying an image compounding technique to TEE images acquired from a mid-esophageal position and a number of transgastric positions in order to reconstruct a high-detail image of the mitral valve and sub-valvular structures. This compounding technique is validated using images acquired of an excised porcine mitral valve. We demonstrate that this compounding technique accurately captures the physical structures present, including the mitral valve, chordae tendineae and papillary muscles.
Assessment of proton beam ablation in myocardial infarct tissue using delayed contrast-enhanced magnetic resonance imaging
Author(s): Maryam E. Rettmann, Stephan Hohmann, Amanda Deisher, Hiroki Konishi, Jon Kruse, Laura Newman, Kay Parker, Michael Herman, Douglas Packer, Mayo Clinic (United States)
Transformation optimization and image blending for 3D liver ultrasound series stitching
Author(s): Yuanyuan Sun, Taygun Kekec, Adriaan Moelker, Wiro J. Niessen, Theo van Walsum, Erasmus MC (Netherlands)
We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher image quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.
Video and Optical Methods for Imaging
William E. Higgins, The Pennsylvania State Univ. (United States) ;
Automatic A-line coronary plaque characterization using combined convolutional and textural features in intravascular OCT images
Author(s): Juhwan Lee, Chaitanya Kolluru, Yazan Gharaibeh, David Prabhu, Case Western Reserve Univ. (United States); Vladislav N. Zimin, Hiram G. Bezerra, Harrington Heart and Vascular Institute, Univ. Hospitals Cleveland Medical Ctr. (United States); David L. Wilson, Case Western Reserve Univ. (United States)
We developed a fully automated method for classifying coronary plaques in intravascular OCT images. The proposed method was trained on a very large number of images with varying lesion characteristics in an attempt to ensure generalizability. Using a hybrid approach (convolutional and textural) features for training/testing, we obtained sensitivity and specificity of 82.2% and 90.8% and 82.4% and 89.2% for fibrolipidic and fibrocalcific A-line classes, respectively. In assessments of clinical impact, automated methods gave good agreement with manually-derived clinically-relevant lesion metrics, and resulted in visual clinical assessments by cardiologists that were unchanged as compared to those from manual labeling.
Motion induced segmentation of stone fragments in ureteroscopy video
Author(s): Soumya Gupta, Sharib Ali, Univ. of Oxford (United Kingdom); Louise Goldsmith, Department of Urology, The Churchill, Oxford University Hospitals NHS Trust, Oxford, UK (United Kingdom); Ben Turney, Department of Urology, The Churchill, Oxford University Hospitals NHS Trust, Oxford (United Kingdom); Jens Rittscher, Univ. of Oxford (United Kingdom)
Ureteroscopy is a conventional procedure used for localization and removal of kidney stones. Often, the surgical team faces tremendous challenge to successfully perform this task, mainly due to poor image quality, presence of floating debris and occlusions in the endoscopy video. Automated localization and segmentation can help to perform stone fragmentation efficiently. However, the automatic segmentation of kidney stones is a complex and challenging procedure due to stone heterogeneity in terms of shape, size, texture, color and position. In addition, dynamic background, motion blur, local deformations, occlusions, varying illumination conditions and visual clutter from the stone debris make the segmentation task even more challenging. We introduce a multi-frame based dense optical flow estimation in a primal-dual optimization framework embedded with a robust data-term based on normalized correlation transform descriptors for segmentation of stone fragments in ureteroscopy data.
Evaluation of real-time guidewire navigation using virtual endoscopic 4D fluoroscopy
Author(s): Brian J. Davis, Martin G. Wagner, Sarvesh Periyasamy, Charles A. Mistretta, Charles M. Strother, Paul F. Laeseke, Michael A. Speidel, Univ. of Wisconsin-Madison (United States)
4D fluoroscopy provides real-time 3D visualization of devices relative to vessels using images from a biplane fluoroscopy system. The utility of 4D-assisted fluoroscopy with endoscopic views and path planning was evaluated relative to conventional 2D-only fluoroscopy in a phantom study where operators navigated a guidewire through a patient-specific cerebral vascular phantom. Users completed 15/15 4D-assisted navigations and 13/15 2D-only navigations. The number of contrast injections performed during guidewire navigation was reduced in 4D-assisted studies. In 4D-assisted navigation, operators reported using 4D views often or almost always, and conventional 2D views never-to-sometimes, with preference towards virtual endoscopic renderings of 4D information.
Towards portable image guidance and automatic patient registration using an RGB-D camera and video projector
Author(s): Colton A. Barr, Andras Lasso, Mark Asselin, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Steve Pieper, Isomics, Inc. (United States); Faith C. Robertson, Brigham and Women's Hospital, Harvard Medical School (United States); William B. Gormley, Brigham and Women's Hospital (United States); Gabor Fichtinger, Lab. for Percutaneous Surgery, Queen's Univ. (Canada)
This study investigated using an RGB-D camera calibrated to a portable projector to provide convenient, inexpensive intraoperative guidance in emergency surgical procedures. We evaluated the accuracy of optical marker tracking, depth-based patient registration, and projected target visualization using open-source software. The results showed that optical marker tracking using the RGB-D camera had significant room for improvement, but that depth-based registration was possible with accuracy sufficient for many bedside interventions. Point projection on a manikin was performed with an average error of 4 mm. Future work will integrate depth data into marker tracking and optimize the system for improved accuracy.
Open source platform for automated collection of training data to support video-based feedback in surgical simulators
Author(s): Jacob Laframboise, Tamas Ungi, Kyle Sunderland, Lab. for Percutaneous Surgery, Queen's Univ. (Canada); Boris Zevin, Queen's Univ. (Canada); Gabor Fichtinger, Lab. for Percutaneous Surgery, Queen's Univ. (Canada)
Surgical training could be improved by automatic surgical video annotation, for which deep learning is promising. The purpose of this research is to demonstrate the use of the 3D Slicer / PLUS Toolkit platform for rapid data collection and deployment of deep learning models. We classified tissue and tool interactions in an open hernia model using deep learning for the tissue and optical tracking for the tools. We successfully deployed this model in 3D Slicer. The 3D Slicer and PLUS Toolkit platform is a powerful platform for deep learning data collection and deployment in open surgery applications.
Improved visual SLAM for bronchoscope tracking and registration with pre-operative CT images
Author(s): Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Nagoya Univ. (Japan); Takayuki Kitasaka, Aichi Institute of Technology (Japan); Hirotoshi Honma, Sapporo Kosei Hospital (Japan); Hirotsugu Takabatake, Minami Sanjyo Hospital (Japan); Masaki Mori, Sapporo Kosei Hospital (Japan); Hiroshi Natori, Nishioka Hospital (Japan); Kensaku Mori, Nagoya Univ. (Japan)
This paper shows an improved visual SLAM for bronchoscope tracking; the result of visual SLAM is used together with CT images for bronchoscopic navigation.
Robot-assisted Image-guided Therapy
Ziv R. Yaniv, National Institute of Allergy and Infectious Diseases (United States) ;
Robotic tissue scanning with biophotonic probe
Author(s): Lauren Yates, Laura Connolly, Amoon Jamzad, Mark Asselin, Rachel Rubino, Scott S. Yam, Tamas Ungi, Andras Lasso, Christopher Nicol, Parvin Mousavi, Gabor Fichtinger, Queen's Univ. (Canada)
The use of Raman spectroscopy for real-time intraoperative tissue classification requires fast analysis with minimal human intervention. In order to have accurate predictions and classifications, a large and reliable database of tissue classifications with spectra results is required.We have developed an automated tissue scanning pattern method to improve the efficiency of robotic optical tissue data collection. The method uses optical images of slides to determine the contour of the biological tissue on the slide, restricting sampling to the tissue and avoiding the slide background. Restricted sampling prevents the machine from spending time on scanning slide points where tissue is not present. This method was implemented in a robotic tissue scanning workflow and compared to a region of interest scanning pattern that encapsulated the entire tissue. Based on the results, this method makes robotic tissue scanning more time efficient.
Image-guided robotic K-Wire placement for orthopaedic trauma surgery
Author(s): Rohan C. Vijayan, Runze Han, Pengwei Wu, Niral M. Sheth, Michael D. Ketcha, Prasad Vagdargi, Johns Hopkins Univ. (United States); Sebastian Vogt, Gerhard Kleinszig, Siemens Healthineers (Germany); Greg M. Osgood, Johns Hopkins Medicine (United States); Jeffrey H. Siewerdsen, Ali Uneri, Johns Hopkins Univ. (United States)
A method for robotic assistance of pelvic fracture fixation is presented, using intraoperative radiographs, preoperative CT, and a robotic end effector to align a K-wire drill guide with planned trajectories. The approach uses 3D-2D registration to resolve the robot pose with respect to patient anatomy from a single radiographic perspective. Experiments in a cadaver study demonstrated registration errors <1.2mm, comparable to tracker-based margins of error (~2 mm). The proposed approach provides an image-based alternative to conventional tracker-based robotic guidance and the use of a single radiographic view facilitates its use with simpler imaging devices, such as non-isocentric C-arms.
A novel mechatronic guidance system for integrated positron emission mammography ultrasound-guided breast biopsy
Author(s): Claire K. Park, Robarts Research Institute (Canada), Western Univ. (Canada); Jeffrey Bax, Lori Gardi, Robarts Research Institute (Canada); Aaron Fenster, Robarts Research Institute (Canada), Western Univ. (Canada)
Image-guided biopsy is crucial for diagnosis and treatment planning of women with breast cancer. Positron emission mammography (PEM) demonstrates high sensitivity and diagnostic accuracy for detecting breast tumours compared to conventional modalities, but anatomical reference and biopsy workflow is not available. This paper reports on the development of a mechatronic guidance system designed to operate with an advanced PEM system and an ultrasound (US)-guided biopsy method. To validate the system, the mechanical guidance arm and integrated biopsy device were assessed for tracking and targeting accuracy. Our system developments show potential for improved biopsy when integrated with PEM and US-guidance.
Fiducial-free 2D/3D registration of the proximal femur for robot-assisted femoroplasty
Author(s): Cong Gao, Robert B. Grupp, Mathias Unberath, Russell H. Taylor, Mehran Armand, Johns Hopkins Univ. (United States)
In this paper, we propose a fiducial-free 2D/3D registration pipeline for navigation of robot assisted femoroplasty, which uses the pelvis to initialize the femur registration. The proposed method was tested through a comprehensive simulation study and achieved a mean accuracy of 2.75 ± 2.19 mm for the femur registration error, which is sufficiently accurate for robotic drill positioning.
Robot-assisted ultrasound imaging for repetitive follow-up assessment of thyroid disease
Author(s): Jakub Tomasz Kaminski, Worcester Polytechnic Institute (United States); Khashayar Rafatzand, Univ. of Massachusetts Medical School (United States); Haichong Zhang, Worcester Polytechnic Institute (United States)
Classical medical ultrasound procedures are performed manually and are highly operator-dependent. A robotic ultrasound procedure, on the other hand, integrates a robotic arm with an ultrasound probe, provides more repeatable results and allows for 3D ultrasound scanning. We present a technique which allows for acquisition of more comparable ultrasound scans targeting thyroid imaging. Our system features a novel robotic ultrasound platform with a 7-axis robotic arm with an ultrasound probe coupled to a 6-DoF force-torque sensor, which allows to adaptively modify the predefined trajectory. We perform ultrasound image processing and registration between multiple trials to counter inter-user and patient variability.
Modeling Applications for Image-guided Therapeutics
Matthieu Chabanas, Univ. Grenoble Alpes (France) ;
Estimating tongue deformation during laryngoscopy using hybrid FEM-multibody model and intraoperative tracking: a cadaver pilot study
Author(s): Xiaotian Wu, Gordon Ctr. for Medical Imaging, Massachusetts General Hospital (United States); C. Antonio Sánchez, John Lloyd, Heather Borgard, Sidney Fels, The Univ. of British Columbia (Canada); Joseph Paydarfar, Dartmouth-Hitchcock Medical Ctr. (United States), Geisel School of Medicine (United States); Ryan Halter, Thayer School of Engineering at Dartmouth (United States)
This paper describes a pipeline that uses preoperative imaging to generate a hybrid FEM-multibody model and then dynamically simulates the deformation of the tongue due to the insertion of a tracked laryngoscope. Target localization errors of 6.3+/-2.8 mm were achieved for the full pipeline as compared to the total tongue deformation between the preoperative and intraoperative CT of a cadaver head, which was 27+/-10 mm. The combination of tracking and deformation modeling is viable to estimate deformation of the tongue during laryngoscopy. Future work involves collecting more data and refining model parameters to improve localization.
The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution
Author(s): Jon S. Heiselman, Michael I. Miga, Vanderbilt Univ. (United States)
Image-guided liver surgery relies on intraoperatively acquired data to create an accurate alignment between image space and physical patient anatomy. However, achieving accurate registration to sparse surface data when deformation is present remains a challenging problem. The image-to-physical liver registration sparse data challenge offers a publicly available dataset for evaluating and comparing potential registration approaches. Using this challenge environment, an inverse biomechanical method for deformable registration to sparse data was investigated to examine how target registration error is impacted by a spatial reconstructive resolution parameter. An optimal model resolution was found that improved error by approximately 32% over previously reported results.
Image data-driven thermal dose prediction for microwave ablation therapy
Author(s): Alice K. Ding, Jon S. Heiselman, Michael I. Miga, Vanderbilt Univ. (United States)
Because many patients diagnosed with hepatocellular carcinoma are not eligible for transplantation or resection, there has been heavy focus on developing locoregional therapies such as thermal ablation, specifically, microwave ablation. To improve the accuracy of localization and delivery of thermal ablation, image guidance can assist by aligning a tracked ablation antenna with a preoperative model of the ablation target. This work will focus on presenting a hepatic phantom with a target mock tumor phantom embedded inside; additionally, it will present the accuracy of ablating this target when using registration methods to predict the movement of the target after deformation.
Modeling the surgical exposure of anatomy in robot-assisted laparoscopic partial nephrectomy
Author(s): Michael A. Kokko, Dartmouth College (United States); John D. Seigne, Dartmouth-Hitchcock Medical Ctr. (United States); Douglas W. Van Citters, Ryan J. Halter, Dartmouth College (United States)
The high degree of mobility and deformability associated with abdominal organs and tissues poses a significant challenge to implementing image guidance for laparoscopic surgery. This work introduces a simulation framework based on clinical data for estimating the location of key structures in robotic partial nephrectomy. Organ exposure is approximated by fusing a segmented CT scan with the pose of an optically tracked endoscope. Additionally, a kidney model suitable for integration into a larger estimation algorithm is developed using CT data from multiple subjects. Visual results of the simulated exposure are presented with goodness of fit data for the kidney model.
Wednesday 19 February Show All Abstracts
AI-based Image Segmentation and Feature Detection
Kensaku Mori, Nagoya Univ. (Japan) ;
CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy
Author(s): Sharmin Sultana, Adam Robinson, Daniel Y. Song, Junghoon Lee, The Johns Hopkins Univ. School of Medicine (United States)
We propose a CNN-based automatic segmentation of pelvic structures in CT images. A hierarchical coarse-to-fine segmentation strategy is used where the first step generates a coarse segmentation from which an organ-specific ROI localization map is produced, and then the second step produces detailed and accurate segmentation of the organs. The network is trained using 100 pelvic CT patient datasets and tested on 15 datasets to segment the prostate, bladder and rectum. The experimental validation shows that the proposed method produces highly accurate segmentation of pelvic structures which can potentially improve the efficiency of radiation therapy planning of prostate cancer treatment.
CondenseUNet: a memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation
Author(s): S. M. Kamrul Hasan, Cristian A. Linte, Rochester Institute of Technology (United States)
We propose a novel memory-efficient CNN architecture as a modification of both CondenseNet, as well as DenseNet for cardiac chambers and myocardium segmentation. Our experiments show that CondenseUNet runs on the ACDC dataset using 2 times less memory than DenseNet, while still maintaining excellent accuracy in estimating clinical parameters. We validated the framework on the ACDC dataset and achieved comparable or even better results than the recent results obtained using the highest performing networks in terms of both Dice score, accuracy, and memory-efficiency.
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?
Author(s): Sven Koehler, Hochschule Mannheim (Germany); Animesh Tandon, Tarique Hussain, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Heiner Latus, Deutsches Herzzentrum München (Germany); Thomas Pickardt, Kompetenznetz Angeborene Herzfehler (Germany); Samir Sarikouch, Kompetenznetz Angeborene Herzfehler (Germany), Medizinische Hochschule Hannover (Germany); Philipp Beerbaum, Medizinische Hochschule Hannover (Germany); Gerald Greil, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Sandy Engelhardt, University Hospital Heidelberg (Germany); Ivo Wolf, Hochschule Mannheim (Germany)
U-Net architectures have shown impressive segmentation results, however, in clinical practice, data sets are more diverse considering individual pathologies and image properties For this work, 1) we assessed the accuracy gap when using a publicly available labelled data set (the AutomaticCardiac Diagnosis Challenge (ACDC) data set) for training and subsequent applying it to CMR data of TOF patients and vice versa and 2) whether we can achieve similar results when applying the model to a more heterogeneous data base. Multiple deep learning models were trained in a cross validation manner. Our results confirm that current deep learning models can achieve excellent results (left ventricle dice of 0.951±0.003/0.941±0.0007 train/validation) within a single data collection. But once they are applied to other pathologies, it becomes apparent how much they overfit to the training pathologies (dice score drops between 0.072±0.001 for the left and 0.165±0.001 for the right ventricle).
Textual fiducial detection in breast conserving surgery for a near-real time image guidance system
Author(s): Winona L. Richey, Jon S. Heiselman, Ma Luo, Vanderbilt Univ. (United States); Ingrid M. Meszoely, Vanderbilt Univ. Medical Ctr. (United States); Michael I. Miga, Vanderbilt Univ. (United States)
Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast conserving surgery (BCS) provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent leads to difficulty in ensuring complete tumor resection. Here we propose a novel BCS image guidance platform that is work-flow friendly, and near-real time. Surface point acquisition must be independent of video framerate, maintain correspondence even for large numbers of fiducials, and be robust to occlusions. The use of character labels in stereo camera images allows for continually updating surface point acquisition. These surface points are incorporated into a four-panel guidance display that includes supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.
A deep learning approach for surgical instruments detection in Orthopaedic surgery videos using transfer learning
Author(s): Belayat Hossain, Shoichi Nishio, Univ. of Hyogo (Japan); Hiranaka Takafumio, Takatsuki General Hospital (Japan); Syoji Kobashi, Univ. of Hyogo (Japan)
Surgical tools detection for intraoperative surgical navigation system is essential for better coordination among surgical team in operating room. Because Orthopaedic surgery (OS) differs from its laparoscopic, due to a large variety of surgical instruments and techniques making its procedures complicated. Compared to usual object detection in natural images, OS video images are confounded by inhomogeneous illumination; it is hard to directly apply existing studies that are developed for others. Additionally, insufficient real surgery data due to few OS cases. We propose a deep learning approach for surgery tools detection in OS videos by integrating knowledge of diverse representative data into the model using transfer learning with natural images of surgical tools.
Journal of Medical Imaging Special Section on Interventional and Surgical Data Science
Amber L. Simpson, Queen's Univ. (Canada) ;
Selected Papers from the Journal of Medical Imaging Special Section
As part of the exciting translational fields in image-guided procedures, robotic interventions and modeling, the scientific committee of the SPIE Medical Imaging conference proposed a special issue to be dedicated to this area in the Journal of Medical Imaging. Authors of accepted papers were invited to SPIE Medical Imaging 2020 to present at the meeting and provide a "technical note" about their accepted paper to the proceedings. Some selected papers were chosen to provide oral presentations in this cutting-edge session. Accepted papers that did not get presented at this session will be asked to present at the poster session.
10:10 AM - 10:30 AM: Introduction provided by Amber Simpson, Queen's Univ. (Canada)
Toward a data-driven predictive modeling framework for guiding locoregional microwave ablation therapy
Author(s): Michael I. Miga, Jarrod A. Collins, Jon S. Heiselman, Vanderbilt Univ. (United States); Daniel B. Brown, Physical Sciences Inc. (United States)
In this work, a data-driven predictive modeling framework is described that combines computational modeling with data from pre- and post-procedural imaging, and intra-procedural therapeutic delivery. The result of the work is to improve localization and predict thermal dose extent during microwave ablation. With respect to the computational model, soft-tissue biomechanics and bioelectric/bioheat transport are used to create a patient-specific scaffold reflecting therapy delivery. Once established, pre-, intra-, and post- procedural data allow the tuning of data-driven predictive dielectric and thermal material models to be used on novel patients. This framework in interventional data science reflects the use of computational models as a biophysical filter to large-scale data sets such that therapeutic predictors can be distilled and used for the delivery of locoregional therapy. Results indicate superior localization and accurate prediction of lesion formation using our data-driven approach to ablation forecasting. Results also provide insight on the impact that localization and material property inaccuracies contribute to the degradation of lesion-to-target overlap.
SpineCloud: image analytics for predictive modeling of spine surgery outcomes
Author(s): Tharindu S. De Silva, National Eye Institute (United States); Sathyanarayana Vedula, Alexander Perdomo-Pantoja, Rohan C. Vijayan, Sophia A. Doerr, Johns Hopkins Univ. (United States); Ali Uneri, Runze Han, The Johns Hopkins Univ. School of Medicine (United States); Michael D. Ketcha, Richard L. Skolasky, Timothy Witham, Johns Hopkins Univ. (United States); Nicholas Theodore, Johns Hopkins Medicine (United States); Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)
Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net
Author(s): François-Xavier Carton, Vanderbilt Univ. (United States); Matthieu Chabanas, Univ. Grenoble Alpes (France); Florian Le Lann, Ctr. Hospitalier Univ. Grenoble Alpes (France); Jack H. Noble, Vanderbilt Univ. (United States)
To compensate for intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative images with intraoperative images. To model tissue resection, the resection cavity needs to be segmented in intraoperative images. In this work, we present an automatic method to segment the resection cavity in intraoperative ultrasound images. We evaluated U-Net networks on two datasets containing images acquired from different ultrasound systems. The best performing method resulted in a 0.72 mean Dice score over the whole dataset. Promising results were obtained for both datasets and motivates further studies with additional clinical data.
A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions
Author(s): Travis L. Williams, Memorial Sloan-Kettering Cancer Ctr. (United States)
This paper contributes to the burgeoning field of surgical data science. Specifically, multi-modal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. We extracted radiomic features from CT scans and combined this with cyst-fluid markers. The cyst fluid model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Radiomic analysis of routinely acquired CT scans combined with cyst fluid inflammatory markers provides accurate prediction of risk of pancreatic cancer progression.
Preoperative prediction of angular insertion depth of lateral wall cochlear implant electrode arrays
Author(s): Mohammad Mahmudur Rahman Khan, Vanderbilt Univ. (United States); Robert F. Labadie, Vanderbilt Univ. Medical Ctr. (United States); Jack H. Noble, Vanderbilt Univ. (United States)
Cochlear implants use an array of electrodes surgically threaded into the cochlea to restore hearing sensation. Techniques for predicting the insertion depth of the array into the cochlea could guide surgeons towards more optimal placement of the array to reduce trauma and preserve the residual hearing. After analyzing the CT images of 86 lateral wall positioned straight electrode array CI recipients, a linear regression model is proposed for insertion depth prediction based on cochlea size, array geometry, and base insertion depth. The model is able to accurately predict angular insertion depths with standard deviation of 41 degrees.
Radiomic analysis from structural MRI for pre-surgical prognostic stratification of glioblastoma patients
Author(s): Spyridon Bakas, Penn Medicine (United States); Gaurav Shukla, Jefferson (United States); Hamed Akbari, Penn Medicine (United States); Guray Erus, Univ. of Pennsylvania (United States); Aristeidis Sotiras, Saima Rathore, Chiharu Sako, Sung Min Ha, Martin Rozycki, Penn Medicine (United States); Russell Takeshi Shinohara, Univ. of Pennsylvania (United States); Michel Bilello, Christos A. Davatzikos, Penn Medicine (United States)
We seek imaging-based overall survival prediction for patients with glioblastoma, the most aggressive brain tumor, to better inform prognosis and potentially influence surgical/treatment decisions. Previous studies have shown promise while integrating structural, perfusion, and diffusion MRI. However, this is not a common acquisition protocol used across multiple institutions, raising generalizability concerns. We report accurate radiomic models utilizing only structural MRI, comparable to those generated from advanced MRI, emphasizing the importance of appropriate feature selection. Our findings show evidence on predicting overall survival based on commonly-acquired structural MRI, and hence potentially generalizable to multi-institutional studies facilitating better patient selection.
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