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Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Nadya Shusharina

    1. Massachusetts General Hospital, Boston, USA

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  2. Mattias P. Heinrich

    1. Universität zu Lübeck, Lübeck, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Ruobing Huang

    1. Shenzhen University, Shenzhen, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12587)

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 2020.

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Table of contents (19 papers)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. ABCs – Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization

      • Nadya Shusharina, Thomas Bortfeld, Carlos Cardenas, Brian De, Kevin Diao, Soleil Hernandez et al.
      Pages 3-15

    3. Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread

      • Xiaoyang Zou, Qi Dou
      Pages 16-26

    4. Ensembled ResUnet for Anatomical Brain Barriers Segmentation

      • Munan Ning, Cheng Bian, Chenglang Yuan, Kai Ma, Yefeng Zheng
      Pages 27-33

    5. An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume

      • Huai Chen, Dahong Qian, Weiping Liu, Hui Li, Lisheng Wang
      Pages 34-39

    6. Automatic Segmentation of Brain Structures for Treatment Planning Optimization and Target Volume Definition

      • Marco Langhans, Tobias Fechter, Dimos Baltas, Harald Binder, Thomas Bortfeld
      Pages 40-48

    7. A Bi-directional, Multi-modality Framework for Segmentation of Brain Structures

      • Skylar S. Gay, Cenji Yu, Dong Joo Rhee, Carlos Sjogreen, Raymond P. Mumme, Callistus M. Nguyen et al.
      Pages 49-57

  3. L2R – Learn2Reg: Multitask and Multimodal 3D Medical Image Registration

    1. Front Matter

      Pages 59-59
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    2. Large Deformation Image Registration with Anatomy-Aware Laplacian Pyramid Networks

      • Tony C. W. Mok, Albert C. S. Chung
      Pages 61-67

    3. Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge

      • Lasse Hansen, Mattias P. Heinrich
      Pages 68-73

    4. Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge

      • Stephanie Häger, Stefan Heldmann, Alessa Hering, Sven Kuckertz, Annkristin Lange
      Pages 74-79

    5. Learning a Deformable Registration Pyramid

      • Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön
      Pages 80-86

    6. Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

      • Théo Estienne, Maria Vakalopoulou, Enzo Battistella, Alexandre Carré, Théophraste Henry, Marvin Lerousseau et al.
      Pages 87-93

    7. Multi-step, Learning-Based, Semi-supervised Image Registration Algorithm

      • Marek Wodzinski
      Pages 94-99

    8. Using Elastix to Register Inhale/Exhale Intrasubject Thorax CT: A Unsupervised Baseline to the Task 2 of the Learn2Reg Challenge

      • Constance Fourcade, Mathieu Rubeaux, Diana Mateus
      Pages 100-105

  4. TN-SCUI – Thyroid Nodule Segmentation and Classification in Ultrasound Images

    1. Front Matter

      Pages 107-107
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    2. Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

      • Mingyu Wang, Chenglang Yuan, Dasheng Wu, Yinghou Zeng, Shaonan Zhong, Weibao Qiu
      Pages 109-115

    3. LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images

      • Huai Chen, Shaoli Song, Xiuying Wang, Renzhen Wang, Deyu Meng, Lisheng Wang
      Pages 116-121

    4. Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation

      • Zhe Tang, Jianqiang Ma
      Pages 122-128
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  8. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

Keywords

About this book

This book constitutes three challenges that were held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020*: the Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, the Learn2Reg Challenge, and the Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge.

The 19 papers presented in this volume were carefully reviewed and selected form numerous submissions. The ABCs challenge aims to identify the best methods of segmenting brain structures that serve as barriers to the spread of brain cancers and structures to be spared from irradiation, for use in computer assisted target definition for glioma and radiotherapy plan optimization. The papers of the L2R challenge cover a wide spectrum of conventional and learning-based registration methods and often describe novel contributions. The main goal of the TN-SCUI challenge is tofind automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images.

*The challenges took place virtually due to the COVID-19 pandemic.

Editors and Affiliations

  • Massachusetts General Hospital, Boston, USA

    Nadya Shusharina

  • Universität zu Lübeck, Lübeck, Germany

    Mattias P. Heinrich

  • Shenzhen University, Shenzhen, China

    Ruobing Huang

Bibliographic Information

  • Book Title: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

  • Book Subtitle: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

  • Editors: Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-71827-5

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-71826-8Published: 13 March 2021

  • eBook ISBN: 978-3-030-71827-5Published: 12 March 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIX, 156

  • Number of Illustrations: 3 b/w illustrations, 54 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Computational Biology/Bioinformatics

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