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Kidney and kidney tumor segmentation...

International Challenge on Kidney and Kidney Tumor Segmentation (2021 :)

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Kidney and kidney tumor segmentation = MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Kidney and kidney tumor segmentation/ edited by Nicholas Heller ... [et al.].
Reminder of title:	MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
remainder title:	MICCAI 2021 Challenge, KiTS 2021
other author:	Heller, Nicholas.
corporate name:	International Challenge on Kidney and Kidney Tumor Segmentation
Published:	Cham :Springer International Publishing : : 2022.,
Description:	viii, 165 p. :ill., digital ;24 cm.
[NT 15003449]:	Automated kidney tumor segmentation with convolution and transformer network -- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations -- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge -- Kidney and kidney tumor segmentation using a two-stage cascade framework -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images -- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- Contrast-Enhanced CT Renal Tumor Segmentation -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge.
Contained By:	Springer Nature eBook
Subject:	Kidneys - Congresses. - Tumors -
Online resource:	https://doi.org/10.1007/978-3-030-98385-7
ISBN:	9783030983857

Kidney and kidney tumor segmentation = MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
Kidney and kidney tumor segmentationMICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /[electronic resource] :MICCAI 2021 Challenge, KiTS 2021edited by Nicholas Heller ... [et al.]. - Cham :Springer International Publishing :2022. - viii, 165 p. :ill., digital ;24 cm. - Lecture notes in computer science,131680302-9743 ;. - Lecture notes in computer science ;13168..

Automated kidney tumor segmentation with convolution and transformer network -- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations -- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge -- Kidney and kidney tumor segmentation using a two-stage cascade framework -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images -- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- Contrast-Enhanced CT Renal Tumor Segmentation -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge.

This book constitutes the Second International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 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.

ISBN: 9783030983857

Standard No.: 10.1007/978-3-030-98385-7doiSubjects--Topical Terms:

3594887
Kidneys
--Tumors--Congresses.

LC Class. No.: RC280.K5 / I58 2021

Dewey Class. No.: 616.99461

Kidney and kidney tumor segmentation = MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
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505 0 $a Automated kidney tumor segmentation with convolution and transformer network -- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations -- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge -- Kidney and kidney tumor segmentation using a two-stage cascade framework -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images -- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- Contrast-Enhanced CT Renal Tumor Segmentation -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge.
520 $a This book constitutes the Second International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 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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700 1 $a Heller, Nicholas. $3 3594885
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711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (24th : $d 2021 : $c Online) $3 3517961
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