Abstract
The KiPA 2022 challenge aims to develop reliable and reproducible methods for segmenting four kidney-related structures on CTA images to improve surgery-based renal cancer treatment. In this work, we describe our approach for 3D segmentation of renal vein, kidney, renal artery, and tumor from the KiPA 2022 dataset. We used the MONAI framework and the SegResNet architecture with a combination of Dice Focal loss and L2 regularization. We applied various data augmentation techniques and trained the model using the AdamW optimizer with a Cosine annealing scheduler. Our method achieved \(10^{th}\) position in the open test leaderboard and \(6^{th}\) position in the closed test leaderboard.
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Siddiquee, M.M.R., Yang, D., He, Y., Xu, D., Myronenko, A. (2023). Automated 3D Segmentation of Renal Structures for Renal Cancer Treatment. In: Xiao, Y., Yang, G., Song, S. (eds) Lesion Segmentation in Surgical and Diagnostic Applications. CuRIOUS KiPA MELA 2022 2022 2022. Lecture Notes in Computer Science, vol 13648. Springer, Cham. https://doi.org/10.1007/978-3-031-27324-7_5
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DOI: https://doi.org/10.1007/978-3-031-27324-7_5
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