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Kidney and Kidney Tumor Segmentation

MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Nicholas Heller

    1. Cleveland Clinic, Cleveland, USA

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  2. Andrew Wood

    1. Cleveland Clinic, Cleveland, USA

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  3. Fabian Isensee

    1. Helmholtz Imaging, Heidelberg, Germany

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  4. Tim Rädsch

    1. Helmholtz Imaging, Heidelberg, Germany

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  5. Resha Teipaul

    1. University of Minnesota, Minneapolis, USA

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  6. Nikolaos Papanikolopoulos

    1. University of Minnesota, Minneapolis, USA

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  7. Christopher Weight

    1. Cleveland Clinic, Cleveland, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14540)

Included in the following conference series:

Conference proceedings info: KiTS 2023.

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Table of contents (22 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge

    • Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu
    Pages 1-7

  3. Exploring 3D U-Net Training Configurations and Post-processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge

    • Kwang-Hyun Uhm, Hyunjun Cho, Zhixin Xu, Seohoon Lim, Seung-Won Jung, Sung-Hoo Hong et al.
    Pages 8-13

  4. Dynamic Resolution Network for Kidney Tumor Segmentation

    • Shuolin Liu, Bing Han
    Pages 14-21

  5. Analyzing Domain Shift When Using Additional Data for the MICCAI KiTS23 Challenge

    • George Stoica, Mihaela Breaban, Vlad Barbu
    Pages 22-29

  6. A Hybrid Network Based on nnU-Net and Swin Transformer for Kidney Tumor Segmentation

    • Lifei Qian, Ling Luo, Yuanhong Zhong, Daidi Zhong
    Pages 30-39

  7. Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts

    • Zohaib Salahuddin, Sheng Kuang, Philippe Lambin, Henry C. Woodruff
    Pages 40-46

  8. An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses

    • Cancan Chen, Rongguo Zhang
    Pages 47-53

  9. Using Uncertainty Information for Kidney Tumor Segmentation

    • Joffrey Michaud, Tewodros Weldebirhan Arega, Stephanie Bricq
    Pages 54-59

  10. Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT Images

    • Soohyun Lee, Hyeyeon Won, Yeeun Lee
    Pages 60-66

  11. GSCA-Net: A Global Spatial Channel Attention Network for Kidney, Tumor and Cyst Segmentation

    • Xiqing Hu, Yanjun Peng
    Pages 67-76

  12. Genetic Algorithm Enhanced nnU-Net for the MICCAI KiTS23 Challenge

    • Tao Li, Di Liu, Bo Yang, Yifan Li, Cheng Zhen
    Pages 77-82

  13. Two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor Segmentation

    • Zhengyu Li, Yanjun Peng, Zengmin Zhang
    Pages 83-92

  14. 3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans

    • Connor Mitchell, Shuwei Xing, Aaron Fenster
    Pages 93-96

  15. Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT Images

    • Andrew Heschl, Hosein Beheshtifard, Phuong Thao Nguyen, Tapotosh Ghosh, Katie Ovens, Farhad Maleki
    Pages 97-106

  16. Cascade UNets for Kidney and Kidney Tumor Segmentation

    • Konstantinos Koukoutegos, Frederik Maes, Hilde Bosmans
    Pages 107-113

  17. Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation

    • Yaqi Wang, Yu Dai, Jianxun Zhang, Jingjing Yin
    Pages 114-119

  18. A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography Scans

    • Kartik Narayan Sahoo, Kumaradevan Punithakumar
    Pages 120-125

  19. Recursive Learning Reinforced by Redefining the Train and Validation Volumes of an Encoder-Decoder Segmentation Model

    • Antonio Vispi
    Pages 126-138

  20. Attention U-Net for Kidney and Masses

    • Duho Lee, Heeyeon Choi
    Pages 139-142
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Keywords

About this book

This book constitutes the Third International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023. The challenge took place in Vancouver, BC, Canada, on October 8, 2023.

The 22 contributions presented in this book were carefully reviewed and selected from 29 submissions.

This challenge aims to develop the best system for automatic semantic segmentation of kidneys, renal tumors and renal cysts.

Editors and Affiliations

  • Cleveland Clinic, Cleveland, USA

    Nicholas Heller, Andrew Wood, Christopher Weight

  • Helmholtz Imaging, Heidelberg, Germany

    Fabian Isensee, Tim Rädsch

  • University of Minnesota, Minneapolis, USA

    Resha Teipaul, Nikolaos Papanikolopoulos

Bibliographic Information

  • Book Title: Kidney and Kidney Tumor Segmentation

  • Book Subtitle: MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Editors: Nicholas Heller, Andrew Wood, Fabian Isensee, Tim Rädsch, Resha Teipaul, Nikolaos Papanikolopoulos, Christopher Weight

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-54806-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Softcover ISBN: 978-3-031-54805-5Published: 07 March 2024

  • eBook ISBN: 978-3-031-54806-2Published: 06 March 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 164

  • Number of Illustrations: 3 b/w illustrations, 73 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computer Applications, Machine Learning

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