Abstract
This project focuses on automatic kidney, tumor and cyst segmentation to assist doctors in diagnosing kidney cancer. We created a deep learning model using methods to first isolate the region of interest of the kidneys, then to segment the kidney and its masses. We used the TotalSegmentator tool to obtain a rough segmentation of the kidneys, then during pre-processing, expanded this region of interest by 18 pixels. This new region of interest was inputted into a 3d segmentation network trained using the nnU-Net library to fully segment the kidneys and masses within them. The current model achieved an average DICE score on validation data of 0.95 for kidney segmentations, and around a 0.8 for tumour and cyst segmentations. On the KiTS23 testing data, the model achieved a 0.94 DICE for kidney segmentations and a 0.73 DICE for mass segmentations.
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References
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.: Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17–48 (2023)
Wasserthal, J., ET AL.: TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv e-prints arXiv:2208.05868 (2022). https://doi.org/10.48550/arXiv.2208.05868
Zettler, N., Mastmeyer, A.: Comparison of 2D vs. 3D U-Net organ segmentation in abdominal 3D CT images. arXiv e-prints arXiv:2107.04062 (Jul 2021). https://doi.org/10.48550/arXiv.2107.04062
Acknowledgment
We would like to acknowledge Ningtao Liu for sharing his PyTorch and convolutional neural network wisdom and tips while developing our workflow, as well as the rest of the Aaron Fenster Lab for helping out when necessary.
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Mitchell, C., Xing, S., Fenster, A. (2024). 3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans. In: Heller, N., et al. Kidney and Kidney Tumor Segmentation. KiTS 2023. Lecture Notes in Computer Science, vol 14540. Springer, Cham. https://doi.org/10.1007/978-3-031-54806-2_13
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DOI: https://doi.org/10.1007/978-3-031-54806-2_13
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