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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 II

  • Conference proceedings
  • © 2022

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 12963)

Included in the following conference series:

Conference proceedings info: BrainLes 2021.

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Table of contents (52 papers)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  2. BraTS

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation

      • Qiran Jia, Hai Shu
      Pages 3-14

    3. Optimized U-Net for Brain Tumor Segmentation

      • Michał Futrega, Alexandre Milesi, Michał Marcinkiewicz, Pablo Ribalta
      Pages 15-29

    4. MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation

      • Parvez Ahmad, Saqib Qamar, Linlin Shen, Syed Qasim Afser Rizvi, Aamir Ali, Girija Chetty
      Pages 30-41

    5. Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database

      • Yading Yuan
      Pages 42-53

    6. Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor Segmentation

      • Kamlesh Pawar, Shenjun Zhong, Dilshan Sasanka Goonatillake, Gary Egan, Zhaolin Chen
      Pages 54-67

    7. Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation

      • Xiaohong Cai, Shubin Lou, Mingrui Shuai, Zhulin An
      Pages 68-79

    8. MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

      • Benjamin B. Yan, Yujia Wei, Jaidip Manikrao M. Jagtap, Mana Moassefi, Diana V. Vera Garcia, Yashbir Singh et al.
      Pages 80-89

    9. Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRI

      • Xue Feng, Harrison Bai, Daniel Kim, Georgios Maragkos, Jan Machaj, Ryan Kellogg
      Pages 90-96

    10. Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation

      • Hai Nguyen-Truong, Quan-Dung Pham
      Pages 97-105

    11. HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR Imaging

      • Haozhe Jia, Chao Bai, Weidong Cai, Heng Huang, Yong Xia
      Pages 106-115

    12. Disparity Autoencoders for Multi-class Brain Tumor Segmentation

      • Chandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade, Divya Reddy, James Holcomb et al.
      Pages 116-124

    13. Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution

      • Zhifan Jiang, Can Zhao, Xinyang Liu, Marius George Linguraru
      Pages 125-137

    14. An Ensemble Approach to Automatic Brain Tumor Segmentation

      • Yaying Shi, Christian Micklisch, Erum Mushtaq, Salman Avestimehr, Yonghong Yan, Xiaodong Zhang
      Pages 138-148

    15. Quality-Aware Model Ensemble for Brain Tumor Segmentation

      • Kang Wang, Haoran Wang, Zeyang Li, Mingyuan Pan, Manning Wang, Shuo Wang et al.
      Pages 149-162

    16. Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs

      • Md Mahfuzur Rahman Siddiquee, Andriy Myronenko
      Pages 163-172

    17. Extending nn-UNet for Brain Tumor Segmentation

      • Huan Minh Luu, Sung-Hong Park
      Pages 173-186

    18. Generalized Wasserstein Dice Loss, Test-Time Augmentation, and Transformers for the BraTS 2021 Challenge

      • Lucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren
      Pages 187-196

    19. Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI

      • Krzysztof Kotowski, Szymon Adamski, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa
      Pages 197-209
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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

Keywords

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.

Editors and Affiliations

  • Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

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