How To Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum Intensity Projections and Diffusion Models
<|reference_start|>How To Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum Intensity Projections and Diffusion Models: Prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging provides a tremendously exciting frontier in visualization of prostate cancer (PCa) metastatic lesions. However, accurate segmentation of metastatic lesions is challenging due to low signal-to-noise ratios and variable sizes, shapes, and locations of the lesions. This study proposes a novel approach for automated segmentation of metastatic lesions in PSMA PET/CT 3D volumetric images using 2D denoising diffusion probabilistic models (DDPMs). Instead of 2D trans-axial slices or 3D volumes, the proposed approach segments the lesions on generated multi-angle maximum intensity projections (MA-MIPs) of the PSMA PET images, then obtains the final 3D segmentation masks from 3D ordered subset expectation maximization (OSEM) reconstruction of 2D MA-MIPs segmentations. Our proposed method achieved superior performance compared to state-of-the-art 3D segmentation approaches in terms of accuracy and robustness in detecting and segmenting small metastatic PCa lesions. The proposed method has significant potential as a tool for quantitative analysis of metastatic burden in PCa patients.<|reference_end|>
@article{toosi2024how,
    title={How To Segment in 3D Using 2D Models: Automated 3D Segmentation of
  Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum
  Intensity Projections and Diffusion Models},
    author={Amirhosein Toosi, Sara Harsini, Franc{c}ois B'enard, Carlos Uribe,
  Arman Rahmim},
    journal={arXiv preprint arXiv:2407.18555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18555},
    primaryClass={physics.med-ph cs.AI cs.CV}
}