Titlebook: Lesion Segmentation in Surgical and Diagnostic Applications; MICCAI 2022 Challeng Yiming Xiao,Guanyu Yang,Shuang Song Conference proceeding

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Md Mahfuzur Rahman Siddiquee,Dong Yang,Yufan He,Daguang Xu,Andriy Myronenko

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卡死偷電

Mortal

臥虎藏龍

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A Segmentation Network Based on 3D U-Net for Automatic Renal Cancer Structure Segmentation in CTA Imved the state-of-the-art, also Dice Similarity Coefficient (DSC) and Average Hausdorff Distance (AVD) of renal artery. According to the results in the KiPA22 challenge, our method have a better segmentation performance in CTA images.

典型

Boundary-Aware Network for?Kidney Parsinge are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65. for kidney structures segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectivene

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CANet: Channel Extending and?Axial Attention Catching Network for?Multi-structure Kidney Segmentatioation. Our solution is founded based on the thriving nn-UNet architecture. Firstly, by extending the channel size, we propose a larger network, which can provide a broader perspective, facilitating the extraction of complex structural information. Secondly, we include an axial attention catching(AAC

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Segmentation of Intra-operative Ultrasound Using Self-supervised Learning Based 3D-ResUnet Model witthis encoder as a pre-trained weight for the Intra-operative ultrasound (iUS) segmentation. In the second stage, the pre-trained weighted-based 3DResUNet proposed model was used to train on the training dataset for iUS segmentation. Experiment on CuRIOUS -22 challenge showed that our proposed soluti

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