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3 April 2024 Weakly supervised detection of pheochromocytomas and paragangliomas in CT
David C. Oluigbo, Bikash Santra, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Abhishek Jha, Mayank Patel, Karel Pacak, Ronald M. Summers
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Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270S (2024) https://doi.org/10.1117/12.3008833
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize. For the management of patients with PPGLs, CT is the preferred modality of choice for precise localization and estimation of their progression. However, due to the myriad variations in size, morphology, and appearance of the tumors in different anatomical regions, radiologists are posed with the challenge of accurate detection of PPGLs. Since clinicians also need to routinely measure their size and track their changes over time across patient visits, manual demarcation of PPGLs is quite a time-consuming and cumbersome process. To ameliorate the manual effort spent for this task, we propose an automated method to detect PPGLs in CT studies via a proxy segmentation task. As only weak annotations for PPGLs in the form of prospectively marked 2D bounding boxes on an axial slice were available, we extended these 2D boxes into weak 3D annotations and trained a 3D full-resolution nnUNet model to directly segment PPGLs. We evaluated our approach on a dataset consisting of chest-abdomen-pelvis CTs of 255 patients with confirmed PPGLs. We obtained a precision of 70% and sensitivity of 64.1% with our proposed approach when tested on 53 CT studies. Our findings highlight the promising nature of detecting PPGLs via segmentation, and furthers the state-of-the-art in this exciting yet challenging area of rare cancer management.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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David C. Oluigbo, Bikash Santra, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Abhishek Jha, Mayank Patel, Karel Pacak, and Ronald M. Summers "Weakly supervised detection of pheochromocytomas and paragangliomas in CT", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270S (3 April 2024); https://doi.org/10.1117/12.3008833
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KEYWORDS
3D modeling
Computed tomography
Cancer
Computer aided detection
Deep learning
Volume segmentation
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