Titlebook: Kidney and Kidney Tumor Segmentation; MICCAI 2021 Challeng Nicholas Heller,Fabian Isensee,Christopher Weight Conference proceedings 2022 Sp

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,Automatic Segmentation in?Abdominal CT Imaging for?the?KiTS21 Challenge,Net which consists of 3D Encoder-Decoder CNN architecture with additional Skip Connection is used. Lastly, there is a loss function to resolve the class imbalance problem frequently occurring in the task of medical imaging. S?rensen-Dice Score and Surface Dice Score on the test set are 80.13 and 68.61.

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Conference proceedings 2022er 27, 2021, due to the COVID-19 pandemic...The 21 contributions presented were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. .

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Modified nnU-Net for the MICCAI KiTS21 Challenge,e 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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3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT,he majority-prediction segmentation masks. Our model achieved test-set performance of 97.0%, 85.1%, and 81.9% volumetric Dice score, and 93.7%, 72.0%, and 70.0% surface Dice score, on combined foreground, renal masses, and renal tumors, respectively, which tied for sixth place among challenge participants.

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