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Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Dajiang Zhu
  • Jingwen Yan
  • Heng Huang
  • Li Shen
  • Paul M. Thompson
  • Carl-Fredrik Westin
  • Xavier Pennec
  • Sarang Joshi
  • Mads Nielsen
  • Tom Fletcher
  • Stanley Durrleman
  • Stefan Sommer
Conference proceedings MBIA 2019, MFCA 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11846)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. MBIA

    1. Front Matter
      Pages 1-1
    2. Wenjuan Wang, Jin Liu, Tengfei Wang, Zongtao Hu, Li Xia, Hongzhi Wang et al.
      Pages 3-11
    3. Jia Liu, Fang Chen, Xianyu Wang, Hongen Liao
      Pages 12-20
    4. Haoteng Tang, Lei Guo, Emily Dennis, Paul M. Thompson, Heng Huang, Olusola Ajilore et al.
      Pages 30-38
    5. Zhibin He, Ying Huang, Tianming Liu, Lei Guo, Lei Du, Tuo Zhang
      Pages 49-56
    6. Ying Huang, Zhibin He, Tianming Liu, Lei Guo, Tuo Zhang
      Pages 57-65
    7. Shijie Zhao, Yan Cui, Yaowu Chen, Xin Zhang, Wei Zhang, Huan Liu et al.
      Pages 66-74
    8. Wei Suo, Xintao Hu, Bowei Yan, Mengyang Sun, Lei Guo, Junwei Han et al.
      Pages 75-83
    9. Yanfu Zhang, Liang Zhan, Paul M. Thompson, Heng Huang
      Pages 84-92
    10. Xiuyan Ni, Tian Gao, Tingting Wu, Jin Fan, Chao Chen
      Pages 93-101
    11. Hongying Liu, Xiongjie Shen, Fanhua Shang, Feihang Ge, Fei Wang
      Pages 102-111
    12. Răzvan V. Marinescu, Arman Eshaghi, Daniel C. Alexander, Polina Golland
      Pages 112-120
    13. Nishant Kumar, Nico Hoffmann, Martin Oelschlägel, Edmund Koch, Matthias Kirsch, Stefan Gumhold
      Pages 121-129
    14. Bo Peng, Zhiyun Ren, Xiaohui Yao, Kefei Liu, Andrew J. Saykin, Li Shen et al.
      Pages 139-148
  3. MFCA

    1. Front Matter
      Pages 149-149
    2. Ayagoz Mussabayeva, Maxim Pisov, Anvar Kurmukov, Alexey Kroshnin, Yulia Denisova, Li Shen et al.
      Pages 151-161
    3. Daniel Tward, Xu Li, Bingxing Huo, Brian Lee, Partha Mitra, Michael Miller
      Pages 162-173
    4. James Fishbaugh, Martin Styner, Karen Grewen, John Gilmore, Guido Gerig
      Pages 174-185
    5. Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang et al.
      Pages 186-195
    6. Youshan Zhang, Jiarui Xing, Miaomiao Zhang
      Pages 196-208
    7. Esfandiar Nava-Yazdani, Hans-Christian Hege, Christoph von Tycowicz
      Pages 209-218
    8. Felix Ambellan, Stefan Zachow, Christoph von Tycowicz
      Pages 219-228
  4. Back Matter
    Pages 229-230

About these proceedings

Introduction

This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected.

The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications.

The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.

Keywords

approximation methods artificial intelligence biomedical imaging computational anatomy image fusion image processing image reconstruction image registration image segmentation imaging genetics machine learning neural networks principal component analysis statistical models statistics of surfaces

Editors and affiliations

  • Dajiang Zhu
    • 1
  • Jingwen Yan
    • 2
  • Heng Huang
    • 3
  • Li Shen
    • 4
  • Paul M. Thompson
    • 5
  • Carl-Fredrik Westin
    • 6
  • Xavier Pennec
    • 7
  • Sarang Joshi
    • 8
  • Mads Nielsen
    • 9
  • Tom Fletcher
    • 10
  • Stanley Durrleman
    • 11
  • Stefan Sommer
    • 12
  1. 1.The University of Texas at ArlingtonArlingtonUSA
  2. 2.Indiana University – Purdue University IndianapolisIndianapolisUSA
  3. 3.University of PittsburghPittsburghUSA
  4. 4.University of PennsylvaniaPhiladelphiaUSA
  5. 5.University of Southern CaliforniaMarina Del ReyUSA
  6. 6.Harvard Medical SchoolBostonUSA
  7. 7.Inria Sophia-AntipolisSophia-AntipolisFrance
  8. 8.University of UtahSalt Lake CityUSA
  9. 9.University of CopenhagenCopenhagenDenmark
  10. 10.University of VirginiaCharlottesvilleUSA
  11. 11.InriaParisFrance
  12. 12.University of CopenhagenCopenhagenDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-33226-6
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-33225-9
  • Online ISBN 978-3-030-33226-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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