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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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  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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Table of contents (27 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. MIDOG

    1. Front Matter

      Pages 1-4
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    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
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    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

Keywords

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.


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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