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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I

  • Conference proceedings
  • © 2022

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Overview

Editors:
  1. Alessandro Crimi

    1. Sano Centre for Computational Personaliz, Kraków, Poland

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    You can also search for this editor in PubMed   Google Scholar

  2. Spyridon Bakas

    1. University of Pennsylvania, Philadelphia, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12962)

Included in the following conference series:

Conference proceedings info: BrainLes 2021.

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About this book

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.

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Table of contents (41 papers)

  1. Front Matter

    Pages i-xxi
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  2. Invited Papers

    1. Front Matter

      Pages 1-1
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    2. A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

      • Alexander Chowdhury, Hasan Kassem, Nicolas Padoy, Renato Umeton, Alexandros Karargyris
      Pages 3-24Open Access
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    3. Opportunities and Challenges for Deep Learning in Brain Lesions

      • Jay Patel, Ken Chang, Syed Rakin Ahmed, Ikbeom Jang, Jayashree Kalpathy-Cramer
      Pages 25-36
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  3. Brain Lesions

    1. Front Matter

      Pages 37-37
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    2. EMSViT: Efficient Multi Scale Vision Transformer for Biomedical Image Segmentation

      • Abhinav Sagar
      Pages 39-51
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    3. CA-Net: Collaborative Attention Network for Multi-modal Diagnosis of Gliomas

      • Baocai Yin, Hu Cheng, Fengyan Wang, Zengfu Wang
      Pages 52-62
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    4. Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI

      • Felix Meissen, Georgios Kaissis, Daniel Rueckert
      Pages 63-74
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    5. Small Lesion Segmentation in Brain MRIs with Subpixel Embedding

      • Alex Wong, Allison Chen, Yangchao Wu, Safa Cicek, Alexandre Tiard, Byung-Woo Hong et al.
      Pages 75-87
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    6. Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation

      • Guillaume Pelluet, Mira Rizkallah, Oscar Acosta, Diana Mateus
      Pages 88-99
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    7. Evaluating Glioma Growth Predictions as a Forward Ranking Problem

      • Karin A. van Garderen, Sebastian R. van der Voort, Maarten M. J. Wijnenga, Fatih Incekara, Georgios Kapsas, Renske Gahrmann et al.
      Pages 100-111
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    8. Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem

      • Martin Žukovec, Lara Dular, Žiga Špiclin
      Pages 112-123
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    9. Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma

      • Yifan Li, Chao Li, Yiran Wei, Stephen Price, Carola-Bibiane Schönlieb, Xi Chen
      Pages 124-139
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    10. Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks

      • Yiran Wei, Yonghao Li, Xi Chen, Carola-Bibiane Schönlieb, Chao Li, Stephen J. Price
      Pages 140-150
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    11. Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments

      • Siddhesh P. Thakur, Sarthak Pati, Ravi Panchumarthy, Deepthi Karkada, Junwen Wu, Dmitry Kurtaev et al.
      Pages 151-167
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  4. BraTS

    1. Front Matter

      Pages 169-169
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    2. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

      • Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi
      Pages 171-181
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    3. Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation

      • Agus Subhan Akbar, Chastine Fatichah, Nanik Suciati
      Pages 182-193
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    4. BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance

      • Polina Druzhinina, Ekaterina Kondrateva, Arseny Bozhenko, Vyacheslav Yarkin, Maxim Sharaev, Anvar Kurmukov
      Pages 194-203
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    5. Combining Global Information with Topological Prior for Brain Tumor Segmentation

      • Hua Yang, Zhiqiang Shen, Zhaopei Li, Jinqing Liu, Jinchao Xiao
      Pages 204-215
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Other volumes

  1. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

  2. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Editors and Affiliations

  • Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

Bibliographic Information

  • Book Title: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

  • Book Subtitle: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I

  • Editors: Alessandro Crimi, Spyridon Bakas

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-08999-2

  • 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 2022

  • Softcover ISBN: 978-3-031-08998-5Published: 22 July 2022

  • eBook ISBN: 978-3-031-08999-2Published: 21 July 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXI, 489

  • Number of Illustrations: 37 b/w illustrations, 134 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Information Systems and Communication Service, Computer Applications

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