Abstract
KiTS21 Challenge is to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. The organizers provide a dataset of 300 cases and each case’s CT scan is segmented to three semantic classes: Kidney, Tumor and Cyst. Compared with KiTS19 Challenge, cyst is a new semantic class, but these two tasks are quite close and that is why we choose nnUNet as our model and made some adjustments on it. Some important changes are made to the original nnUNet to adapt to this new task. Furthermore, we train models in 3 different ways and finally and merge them into one model by specific strategies. Detailed information is available in the part of Methods. The organizer uses an evaluation method called “Hierarchical Evaluation Classes” (HECs). The HEC scores of each model are showed in the following .
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Southeast University: A College in China. Xu Lizhan, Shi Jiacheng, and Dong Zhangfu: First-Year Graduate Students in Southeast University
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Xu, L., Shi, J., Dong, Z. (2022). Modified nnU-Net for the MICCAI KiTS21 Challenge. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_3
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DOI: https://doi.org/10.1007/978-3-030-98385-7_3
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