3D U-Net with region of interest segmentation of kidneys and masses in computed tomography scans

Connor Mitchell; Shuwei Xing; Derek W. Cool; David Tessier; Aaron Fenster

doi:10.1117/12.3008783

30 March 2024 3D U-Net with region of interest segmentation of kidneys and masses in computed tomography scans

Connor Mitchell, Shuwei Xing, Derek W. Cool, David Tessier, Aaron Fenster

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129282E (2024) https://doi.org/10.1117/12.3008783
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

CT-guided renal tumor ablations have been considered an alternative to treat small renal tumors, typically 4 cm in size or smaller, especially for patients who are ineligible to receive nephron-sparing surgery. For this procedure, the radiologist must compare the pre-operative with the post-operative CT to determine the presence of residual tumors. Distinguishing between malignant and benign kidney tumors poses a significant challenge. To automate this tumor coverage evaluation step and assist the radiologist in identifying kidney tumors, we proposed a coarse-to-fine U-Net-based model to segment kidneys and masses. We used the TotalSegmentator tool to obtain an approximate segmentation and region of interest of the kidneys, which was inputted into our 3D segmentation network trained using the nnUNet library to fully segment the kidneys and masses within them. Our model achieved an aggregated DICE score of 0.777 on testing data, and on local CT kidney data with tumors collected from the London Health Sciences University Hospital, our model achieved a DICE score of 0.7 for tumour segmentation. Our results indicate the model will be useful for tumour identification and evaluation.

Citation Download Citation

Connor Mitchell, Shuwei Xing, Derek W. Cool, David Tessier, and Aaron Fenster "3D U-Net with region of interest segmentation of kidneys and masses in computed tomography scans", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129282E (30 March 2024); https://doi.org/10.1117/12.3008783

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