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

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Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Imageffective control methods. The precise and automatic segmentation of kidney tumors in computed tomography (CT) is an important prerequisite for medical methods such as pathological localization and radiotherapy planning, However, due to the large differences in the shape, size, and location of kidney

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,A Two-Stage Cascaded Deep Neural Network with?Multi-decoding Paths for?Kidney Tumor Segmentation,or kidney cancer diagnosis. Automatic and accurate kidney and kidney tumor segmentation in CT scans is crucial for treatment and surgery planning. However, kidney tumors and cysts have various morphologies, with blurred edges and unpredictable positions. Therefore, precise segmentation of tumors and

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,Automatic Segmentation in?Abdominal CT Imaging for?the?KiTS21 Challenge,t. Convolutional Neural Network is trained in patches of three-dimensional abdominal CT imaging. For the segmentation of the 3D image, a variant of U-Net which consists of 3D Encoder-Decoder CNN architecture with additional Skip Connection is used. Lastly, there is a loss function to resolve the cla

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Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on Cnced computed tomography (CT). A total of 300 kidney cancer patients with contrast-enhanced CT scans and clinical characteristics were included. A baseline segmentation of the kidney cancer was performed using a 3D U-Net. Input to the U-Net were the contrast-enhanced CT images, output were segmentat

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