Abstract
In this paper we present our approach for the KiTS21 Challenge. The goal is to automatically segment kidneys, (renal) tumors and (renal) cysts based on 3D computed tomography (CT) images of the abdomen. The challenge provided public training 300 cases for this purpose. To solve this problem, we used a 3D U-ResNet with pre- and postprocessing and data augmentation. The preprocessing includes the overlap-tile strategy by preparing the input patches, while a rule-based postprocessing was applied to remove false-positive artefacts. Our model achieved 0.812 average dice, 0.694 average surface dice and 0.7 tumor dice. This led to the 12.5th position in the KiTS21 challenge.
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Adam, J. et al. (2022). Extraction of Kidney Anatomy Based on a 3D U-ResNet with Overlap-Tile Strategy. 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_2
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DOI: https://doi.org/10.1007/978-3-030-98385-7_2
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