Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings | SpringerLink
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Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis

MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Marc Aubreville

    1. Technische Hochschule Ingolstadt, Ingolstadt, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. David Zimmerer

    1. German Cancer Research Center (DKFZ), Heidelberg, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Mattias Heinrich

    1. Universität zu Lübeck, Lübeck, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13166)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MICCAI 2021.

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About this book

This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic.

The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges:

  •          Mitosis Domain Generalization Challenge (MIDOG 2021),
  •          Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and
  •          Learn2Reg (L2R 2021).

The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.


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Table of contents (27 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. MIDOG

    1. Front Matter

      Pages 1-4
      Download chapter PDF

    2. Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge

      • Frauke Wilm, Christian Marzahl, Katharina Breininger, Marc Aubreville
      Pages 5-13

    3. Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images

      • Jack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
      Pages 14-22

    4. Domain-Robust Mitotic Figure Detection with Style Transfer

      • Youjin Chung, Jihoon Cho, Jinah Park
      Pages 23-31

    5. Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images

      • Ramin Nateghi, Fattaneh Pourakpour
      Pages 32-39

    6. Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection

      • Rutger H. J. Fick, Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit, Saima Ben Hadj
      Pages 40-47

    7. Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

      • Mostafa Jahanifar, Adam Shepard, Neda Zamanitajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram et al.
      Pages 48-52

    8. MitoDet: Simple and Robust Mitosis Detection

      • Jakob Dexl, Michaela Benz, Volker Bruns, Petr Kuritcyn, Thomas Wittenberg
      Pages 53-57

    9. Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection

      • Satoshi Kondo
      Pages 58-61

    10. Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge

      • Maxime W. Lafarge, Viktor H. Koelzer
      Pages 62-67

    11. Detecting Mitosis Against Domain Shift Using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge

      • Jingtang Liang, Cheng Wang, Yujie Cheng, Zheng Wang, Fang Wang, Liyu Huang et al.
      Pages 68-72

    12. Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

      • Xi Long, Ying Cheng, Xiao Mu, Lian Liu, Jingxin Liu
      Pages 73-76

    13. Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers

      • Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi
      Pages 77-80

    14. Cascade R-CNN for MIDOG Challenge

      • Salar Razavi, Fariba Dambandkhameneh, Dimitri Androutsos, Susan Done, April Khademi
      Pages 81-85

    15. Sk-Unet Model with Fourier Domain for Mitosis Detection

      • Sen Yang, Feng Luo, Jun Zhang, Xiyue Wang
      Pages 86-90

  3. MOOD

    1. Front Matter

      Pages 91-94
      Download chapter PDF

    2. Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation

      • Jihoon Cho, Inha Kang, Jinah Park
      Pages 95-103

    3. Self-supervised Medical Out-of-Distribution Using U-Net Vision Transformers

      • Seongjin Park, Adam Balint, Hyejin Hwang
      Pages 104-110

    4. SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes

      • Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
      Pages 111-118
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  8. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

  9. Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis

Editors and Affiliations

  • Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Marc Aubreville

  • German Cancer Research Center (DKFZ), Heidelberg, Germany

    David Zimmerer

  • Universität zu Lübeck, Lübeck, Germany

    Mattias Heinrich

Bibliographic Information

  • Book Title: Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis

  • Book Subtitle: MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27–October 1, 2021, Proceedings

  • Editors: Marc Aubreville, David Zimmerer, Mattias Heinrich

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-97281-3

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-030-97280-6Published: 02 March 2022

  • eBook ISBN: 978-3-030-97281-3Published: 01 March 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: IX, 194

  • Number of Illustrations: 17 b/w illustrations, 51 illustrations in colour

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

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