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Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation

MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Jun Ma

    1. University of Toronto, Toronto, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Bo Wang

    1. University of Toronto, Totonto, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13816)

Included in the following conference series:

Conference proceedings info: FLARE 2022.

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About this book

This book constitutes the proceedings of the MICCAI 2022 Challenge, FLARE 2022, held in Conjunction with MICCAI 2022, in Singapore, on September 22, 2022.

The 28 full papers presented in this book were carefully reviewed and selected from 48 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.

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Keywords

Table of contents (28 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Self-training with Selective Re-training Improves Abdominal Organ Segmentation in CT Image

    • Fan Zhang, Meihuan Wang, Hua Yang
    Pages 1-10

  3. Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy

    • YuanKe Pan, Jinxin Zhu, Bingding Huang
    Pages 11-22

  4. Abdominal CT Organ Segmentation by Accelerated nnUNet with a Coarse to Fine Strategy

    • Shoujin Huang, Lifeng Mei, Jingyu Li, Ziran Chen, Yue Zhang, Tan Zhang et al.
    Pages 23-34

  5. Semi-supervised Detection, Identification and Segmentation for Abdominal Organs

    • Mingze Sun, Yankai Jiang, Heng Guo
    Pages 35-46

  6. An Efficiency Coarse-to-Fine Segmentation Framework for Abdominal Organs Segmentation

    • Cancan Chen, Weixin Xu, Rongguo Zhang
    Pages 47-55

  7. Semi-supervised Augmented 3D-CNN for FLARE22 Challenge

    • Zining Chen, Tianyi Wang, Shihao Han, Yinan Song, Shichao Li
    Pages 56-63

  8. DLUNet: Semi-supervised Learning Based Dual-Light UNet for Multi-organ Segmentation

    • Haoran Lai, Tao Wang, Shuoling Zhou
    Pages 64-73

  9. Multi-organ Segmentation Based on 2.5D Semi-supervised Learning

    • Hao Chen, Wen Zhang, Xiaochao Yan, Yanbin Chen, Xin Chen, Mengjun Wu et al.
    Pages 74-86

  10. 3D Cross-Pseudo Supervision (3D-CPS): A Semi-supervised nnU-Net Architecture for Abdominal Organ Segmentation

    • Yongzhi Huang, Hanwen Zhang, Yan Yan, Haseeb Hassan
    Pages 87-100

  11. Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-supervised Abdominal Organ Segmentation in CT

    • Jae Won Choi
    Pages 101-115

  12. Uncertainty-Guided Self-learning Framework for Semi-supervised Multi-organ Segmentation

    • Natália Alves, Bram de Wilde
    Pages 116-127

  13. A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation

    • Gregor Koehler, Fabian Isensee, Klaus Maier-Hein
    Pages 128-138

  14. CLEF: Contrastive Learning of Equivariant Features in CT Images

    • Ilya Kuleshov, Mikhail Goncharov, Vera Soboleva
    Pages 139-151

  15. Teacher-Student Semi-supervised Approach for Medical Image Segmentation

    • Maria Baldeon Calisto
    Pages 152-162

  16. Semi-supervised Organ Segmentation with Mask Propagation Refinement and Uncertainty Estimation for Data Generation

    • Minh-Khoi Pham, Thang-Long Nguyen-Ho, Thao Thi Phuong Dao, Tan-Cong Nguyen, Minh-Triet Tran
    Pages 163-177

  17. Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference

    • Ziyan Huang, Haoyu Wang, Jin Ye, Jingqi Niu, Can Tu, Yuncheng Yang et al.
    Pages 178-189

  18. A Simple Mean-Teacher UNet Model for Efficient Abdominal Organ Segmentation

    • Zixiao Zhao, Jiahua Chu
    Pages 190-201

  19. Cascade Dual-decoders Network for Abdominal Organs Segmentation

    • Ershuai Wang, Yaliang Zhao, Yajun Wu
    Pages 202-213

  20. Semi-supervised 3D U-Net Learning Based on Meta Pseudo Labels

    • Chuda Xiao, Zhuo Chen, Haoyu Li, Dan Li, Rashid Khan, Jinyu Tian et al.
    Pages 214-222
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  1. Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation

Editors and Affiliations

  • University of Toronto, Toronto, Canada

    Jun Ma

  • University of Toronto, Totonto, Canada

    Bo Wang

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