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Connectomics in NeuroImaging

Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

  • Markus D. Schirmer
  • Archana Venkataraman
  • Islem Rekik
  • Minjeong Kim
  • Ai Wern Chung
Conference proceedings CNI 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11848)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11848)

Table of contents

  1. Front Matter
    Pages i-x
  2. Zhongwei Huang, Haijun Lei, Guoliang Chen, Shiqi Li, Hancong Li, Ahmed Elazab et al.
    Pages 1-9
  3. Naresh Nandakumar, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman
    Pages 10-20
  4. Nicolas Honnorat, Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan, Kilian Pohl
    Pages 32-41
  5. Moo K. Chung, Linhui Xie, Shih-Gu Huang, Yixian Wang, Jingwen Yan, Li Shen
    Pages 42-53
  6. Javid Dadashkarimi, Siyuan Gao, Erin Yeagle, Stephanie Noble, Dustin Scheinost
    Pages 64-73
  7. Anvar Kurmukov, Yuji Zhao, Ayagoz Mussabaeva, Boris Gutman
    Pages 106-116
  8. Michael Hütel, Michela Antonelli, Jinendra Ekanayake, Sebastien Ourselin, Andrew Melbourne
    Pages 117-125
  9. Back Matter
    Pages 139-139

About these proceedings

Introduction

This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019.

The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.

Keywords

artificial intelligence brain connectivity classification data mining diffusion MRI feature selection functional MRI image analysis image processing image reconstruction image segmentation neural networks pattern recognition principal component analysis statistical methods support vector machines SVM

Editors and affiliations

  1. 1.Harvard Medical SchoolBostonUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.Istanbul Technical UniversityIstanbulTurkey
  4. 4.University of North CarolinaGreensboroUSA
  5. 5.Harvard Medical SchoolBostonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-32391-2
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-32390-5
  • Online ISBN 978-3-030-32391-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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