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

5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Alessandro Crimi

    1. University Hospital of Zurich, Zurich, Switzerland

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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 11993)

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 2019.

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Table of contents (38 papers)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Brain Tumor Image Segmentation

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Brain Tumor Segmentation Using Attention-Based Network in 3D MRI Images

      • Xiaowei Xu, Wangyuan Zhao, Jun Zhao
      Pages 3-13

    3. Multimodal Brain Image Segmentation and Analysis with Neuromorphic Attention-Based Learning

      • Woo-Sup Han, Il Song Han
      Pages 14-26

    4. Improving Brain Tumor Segmentation in Multi-sequence MR Images Using Cross-Sequence MR Image Generation

      • Guojing Zhao, Jianpeng Zhang, Yong Xia
      Pages 27-36

    5. Ensemble of CNNs for Segmentation of Glioma Sub-regions with Survival Prediction

      • Subhashis Banerjee, Harkirat Singh Arora, Sushmita Mitra
      Pages 37-49

    6. Brain Tumor Segmentation Based on Attention Mechanism and Multi-model Fusion

      • Xutao Guo, Chushu Yang, Ting Ma, Pengzheng Zhou, Shangfeng Lu, Nan Ji et al.
      Pages 50-60

    7. Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

      • Shuo Wang, Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
      Pages 61-72

    8. Multimodal Brain Tumor Segmentation and Survival Prediction Using Hybrid Machine Learning

      • Linmin Pei, Lasitha Vidyaratne, M. Monibor Rahman, Zeina A. Shboul, Khan M. Iftekharuddin
      Pages 73-81

    9. Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs

      • Andriy Myronenko, Ali Hatamizadeh
      Pages 82-89

    10. Brain Tumor Segmentation with Cascaded Deep Convolutional Neural Network

      • Ujjwal Baid, Nisarg A. Shah, Sanjay Talbar
      Pages 90-98

    11. Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI

      • Chandan Ganesh Bangalore Yogananda, Ben Wagner, Sahil S. Nalawade, Gowtham K. Murugesan, Marco C. Pinho, Baowei Fei et al.
      Pages 99-112

    12. 3D Automatic Brain Tumor Segmentation Using a Multiscale Input U-Net Network

      • S. Rosas González, T. Birgui Sekou, M. Hidane, C. Tauber
      Pages 113-123

    13. Semi-supervised Variational Autoencoder for Survival Prediction

      • Sveinn Pálsson, Stefano Cerri, Andrea Dittadi, Koen Van Leemput
      Pages 124-134

    14. Multi-modal U-Nets with Boundary Loss and Pre-training for Brain Tumor Segmentation

      • Pablo Ribalta Lorenzo, Michal Marcinkiewicz, Jakub Nalepa
      Pages 135-147

    15. Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

      • Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei et al.
      Pages 148-157

    16. Hybrid Labels for Brain Tumor Segmentation

      • Parvez Ahmad, Saqib Qamar, Seyed Raein Hashemi, Linlin Shen
      Pages 158-166

    17. Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Patient Survival

      • Thibault Buatois, Élodie Puybareau, Guillaume Tochon, Joseph Chazalon
      Pages 167-178

    18. Detection and Segmentation of Brain Tumors from MRI Using U-Nets

      • Krzysztof Kotowski, Jakub Nalepa, Wojciech Dudzik
      Pages 179-190

    19. Multimodal Segmentation with MGF-Net and the Focal Tversky Loss Function

      • Nabila Abraham, Naimul Mefraz Khan
      Pages 191-198
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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

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019.

The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (57 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.)

Editors and Affiliations

  • University Hospital of Zurich, Zurich, Switzerland

    Alessandro Crimi

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

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