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

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Modified nnU-Net for the MICCAI KiTS21 Challenge,ataset 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

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2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst,works to automatically segment kidney and tumor and cyst in computed tomography (CT) images. First, the kidney is pre-segmented by the first stage network ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-process

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Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations,on tasks. We argue that the skip connections between the encoder and decoder layers pass too many redundant information, and filtered out the unnecessary information may be helpful in improving the segmentation accuracy. In this paper, we proposed a contrast attention mechanism at the skip connectio

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,A Coarse-to-Fine Framework for?the?2021 Kidney and?Kidney Tumor Segmentation Challenge,tool for kidney cancer surgery. In this paper, we use a coarse-to-fine framework which is based on the nnU-Net and achieve accurate and fast segmentation of the kidney and kidney mass. The average Dice and surface Dice of segmentation predicted by our method on the test are 0.9077 and 0.8262, respec

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