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ORCIDKiTS@MICCAI 2021: Strasbourg, France
- Nicholas Heller
, Fabian Isensee, Darya Trofimova, Resha Tejpaul, Nikolaos Papanikolopoulos, Christopher Weight:
Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Lecture Notes in Computer Science 13168, Springer 2022, ISBN 978-3-030-98384-0 - Zhiqiang Shen, Hua Yang, Zhen Zhang, Shaohua Zheng:
Automated Kidney Tumor Segmentation with Convolution and Transformer Network. 1-12 - Jannes Adam, Niklas Agethen, Robert Bohnsack, René Finzel, Timo Günnemann, Lena Philipp, Marcel Plutat, Markus Rink, Tingting Xue, Felix Thielke, Hans Meine:
Extraction of Kidney Anatomy Based on a 3D U-ResNet with Overlap-Tile Strategy. 13-21 - Lizhan Xu, Jiacheng Shi, Zhangfu Dong:
Modified nnU-Net for the MICCAI KiTS21 Challenge. 22-27 - Zhiwei Chen, Hanqiang Liu:
2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst. 28-34 - Vivek Pawar, Bharadwaj Kss:
Automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans. 35-39 - Yi Lv, Junchen Wang:
Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. 40-45 - Mengran Wu, Zhiyang Liu:
Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations. 46-52 - Zhongchen Zhao, Huai Chen, Lisheng Wang:
A Coarse-to-Fine Framework for the 2021 Kidney and Kidney Tumor Segmentation Challenge. 53-58 - Chaonan Lin, Rongda Fu, Shaohua Zheng:
Kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework. 59-70 - Jianhui Wen, Zhaopei Li, Zhiqiang Shen, Yaoyong Zheng, Shaohua Zheng:
Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images. 71-79 - Tian He, Zhen Zhang, Chenhao Pei, Liqin Huang:
A Two-Stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation. 80-89 - Matej Gazda, Peter Bugata, Jakub Gazda, David Hubacek, David Jozef Hresko, Peter Drotár:
Mixup Augmentation for Kidney and Kidney Tumor Segmentation. 90-97 - Jimin Heo:
Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge. 98-102 - Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon:
An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans. 103-115 - Chuda Xiao, Haseeb Hassan, Bingding Huang:
Contrast-Enhanced CT Renal Tumor Segmentation. 116-122 - Dan Li, Zhuo Chen, Haseeb Hassan, Weiguo Xie, Bingding Huang:
A Cascaded 3D Segmentation Model for Renal Enhanced CT Images. 123-128 - Christina B. Lund, Bas H. M. van der Velden:
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT. 129-136 - Yasmeen M. George:
A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans. 137-142 - Mingyang Zang, Artur Wysoczanski, Elsa D. Angelini, Andrew F. Laine:
3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT. 143-150 - Sajan Gohil, Abhi Lad:
Kidney and Kidney Tumor Segmentation Using Spatial and Channel Attention Enhanced U-Net. 151-157 - Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia:
Transfer Learning for KiTS21 Challenge. 158-163
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