text: Download PDFOpen PDF in browserA deep learning-based approach for localization of pedicle regions in preoperative CT scans5 pages•Published: July 12, 2018AbstractPedicle screw fixation is a common yet technically demanding procedure. Due to the proximity of the inserted implant to the spinal column, a malplaced screw can cause neurological injury and subsequent postoperative complications. A common surgical routine starts with preoperative volumetric image acquisition (e.g. computed tomography) based on which the surgeons can highlight the planned trajectory. This process is generally done manually , which is error prone and time consuming.The primary purpose of this paper is to develop an automatic pedicle region localization based on preoperative CTs. This system can automatically annotate the CT scans to identify the regions corresponding to the pedicles and thus provide important information about the anatomical placement of the CT scan that can be useful for intraoperative implant position assessment (e.g. to initialize the 2D-3D registration). On the other hand, the pedicle localization can be exploited for preoperative planning. We designed and evaluated a fully convolutional neural network for the task of pedicle localization. A large training, validation and testing datasets (5000, 1000, 1000 images separately) were created using a custom data augmentation process that could generate unique vertebral morphologies for each image. After evaluation on the validation and test data, the Dice similarity coefficients between the pedicle regions detected by the trained network and the ground truth was 0.85 and 0.83 respectively. The proposed deep-learning-based algorithm was capable of automatically localizing the regions corresponding to the pedicles based on the preoperative CT scans. Therefore, a reliable initial guess for the 2D-3D registration process needed for intraoperative implant position assessment can be achieved. This system also has potential use in automating the preoperative planning. In: Wei Tian and Ferdinando Rodriguez Y Baena (editors). CAOS 2018. The 18th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 2, pages 46-50.