Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866917589084536832 |
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| author | Oluigboa, David C. Santra, Bikash Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Jha, Abhishek Patel, Mayank Pacak, Karel Summers, Ronald M. |
| author_facet | Oluigboa, David C. Santra, Bikash Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Jha, Abhishek Patel, Mayank Pacak, Karel Summers, Ronald M. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_08697 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT Oluigboa, David C. Santra, Bikash Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Jha, Abhishek Patel, Mayank Pacak, Karel Summers, Ronald M. Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.08697 |