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

6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Alessandro Crimi

    1. University of Zurich, Zurich, Switzerland

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  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 12658)

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

Included in the following conference series:

Conference proceedings info: BrainLes 2020.

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About this book

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.*

The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions).

*The workshop and challenges were held virtually.




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Table of contents (46 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Invited Papers

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Glioma Diagnosis and Classification: Illuminating the Gold Standard

      • MacLean P. Nasrallah
      Pages 3-10

    3. Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods

      • Huahong Zhang, Ipek Oguz
      Pages 11-29

    4. Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics

      • Anahita Fathi Kazerooni, Christos Davatzikos
      Pages 30-38

  3. Brain Lesion Image Analysis

    1. Front Matter

      Pages 39-39
      Download chapter PDF

    2. Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks

      • Zhongqiang Liu, Dongdong Gu, Yu Zhang, Xiaohuan Cao, Zhong Xue
      Pages 41-50

    3. Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic Angiography

      • Guoqing Wu, Liqiong Zhang, Xi Chen, Jixian Lin, Yuanyuan Wang, Jinhua Yu
      Pages 51-59

    4. Volume Preserving Brain Lesion Segmentation

      • Yanlin Liu, Xiangzhu Zeng, Chuyang Ye
      Pages 60-69

    5. Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple Sclerosis

      • Lorenza Brusini, Ilaria Boscolo Galazzo, Muge Akinci, Federica Cruciani, Marco Pitteri, Stefano Ziccardi et al.
      Pages 70-79

    6. Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology

      • Xiaofeng Liu, Fangxu Xing, Chao Yang, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
      Pages 80-91

    7. Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping

      • Lucas Martin, Julie Josse, Bertrand Thirion
      Pages 92-100

    8. Label-Efficient Multi-task Segmentation Using Contrastive Learning

      • Junichiro Iwasawa, Yuichiro Hirano, Yohei Sugawara
      Pages 101-110

    9. Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

      • Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim et al.
      Pages 111-121

    10. MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection

      • Hui Yu, Wenjun Xia, Yan Liu, Xuejun Gu, Jiliu Zhou, Yi Zhang
      Pages 122-132

    11. Unsupervised 3D Brain Anomaly Detection

      • Jaime Simarro Viana, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana M. Sima, CENTER-TBI Participants and Investigators
      Pages 133-142

    12. Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI

      • Tejas Sudharshan Mathai, Yi Wang, Nathan Cross
      Pages 143-156

    13. Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression

      • Sarthak Pati, Vaibhav Sharma, Heena Aslam, Siddhesh P. Thakur, Hamed Akbari, Andreas Mang et al.
      Pages 157-167

    14. Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions

      • Julian Klug, Guillaume Leclerc, Elisabeth Dirren, Maria Giulia Preti, Dimitri Van De Ville, Emmanuel Carrera
      Pages 168-180

  4. Brain Tumor Segmentation

    1. Front Matter

      Pages 181-181
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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

  • University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

Bibliographic Information

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

  • Book Subtitle: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I

  • Editors: Alessandro Crimi, Spyridon Bakas

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-72084-1

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-72083-4Published: 27 March 2021

  • eBook ISBN: 978-3-030-72084-1Published: 26 March 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XX, 529

  • Number of Illustrations: 17 b/w illustrations, 180 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Pattern Recognition, Computational Biology/Bioinformatics

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