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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part I

Conference proceedings info: MICCAI 2016.

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Table of contents (77 papers)

  1. Front Matter

    Pages I-XLIV
  2. Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis

    • Mingxia Liu, Junqiang Du, Biao Jie, Daoqiang Zhang
    Pages 1-9
  3. Discovering Cortical Folding Patterns in Neonatal Cortical Surfaces Using Large-Scale Dataset

    • Yu Meng, Gang Li, Li Wang, Weili Lin, John H. Gilmore, Dinggang Shen
    Pages 10-18
  4. Modeling Functional Dynamics of Cortical Gyri and Sulci

    • Xi Jiang, Xiang Li, Jinglei Lv, Shijie Zhao, Shu Zhang, Wei Zhang et al.
    Pages 19-27
  5. A Multi-stage Sparse Coding Framework to Explore the Effects of Prenatal Alcohol Exposure

    • Shijie Zhao, Junwei Han, Jinglei Lv, Xi Jiang, Xintao Hu, Shu Zhang et al.
    Pages 28-36
  6. Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification

    • Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
    Pages 37-45
  7. Temporal Concatenated Sparse Coding of Resting State fMRI Data Reveal Network Interaction Changes in mTBI

    • Jinglei Lv, Armin Iraji, Fangfei Ge, Shijie Zhao, Xintao Hu, Tuo Zhang et al.
    Pages 46-54
  8. Exploring Brain Networks via Structured Sparse Representation of fMRI Data

    • Qinghua Zhao, Jianfeng Lu, Jinglei Lv, Xi Jiang, Shijie Zhao, Tianming Liu
    Pages 55-62
  9. Discover Mouse Gene Coexpression Landscape Using Dictionary Learning and Sparse Coding

    • Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng et al.
    Pages 63-71
  10. Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma

    • Ju Han, Yunfu Wang, Weidong Cai, Alexander Borowsky, Bahram Parvin, Hang Chang
    Pages 72-80
  11. Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-Sectional Multi-site MRI

    • Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E. Cutting, Bennett A. Landman
    Pages 81-88
  12. Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach

    • Demian Wassermann, Dorian Mazauric, Guillermo Gallardo-Diez, Rachid Deriche
    Pages 89-96
  13. Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification

    • Yingying Zhu, Xiaofeng Zhu, Han Zhang, Wei Gao, Dinggang Shen, Guorong Wu
    Pages 106-114
  14. Boundary Mapping Through Manifold Learning for Connectivity-Based Cortical Parcellation

    • Salim Arslan, Sarah Parisot, Daniel Rueckert
    Pages 115-122
  15. Species Preserved and Exclusive Structural Connections Revealed by Sparse CCA

    • Xiao Li, Lei Du, Tuo Zhang, Xintao Hu, Xi Jiang, Lei Guo et al.
    Pages 123-131
  16. Modularity Reinforcement for Improving Brain Subnetwork Extraction

    • Chendi Wang, Bernard Ng, Rafeef Abugharbieh
    Pages 132-139
  17. Effective Brain Connectivity Through a Constrained Autoregressive Model

    • Alessandro Crimi, Luca Dodero, Vittorio Murino, Diego Sona
    Pages 140-147
  18. GraMPa: Graph-Based Multi-modal Parcellation of the Cortex Using Fusion Moves

    • Sarah Parisot, Ben Glocker, Markus D. Schirmer, Daniel Rueckert
    Pages 148-156
  19. A Continuous Model of Cortical Connectivity

    • Daniel Moyer, Boris A. Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M. Thompson
    Pages 157-165

About this book

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016.

Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis; brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.



Editors and Affiliations

  • University College London , London, United Kingdom

    Sebastien Ourselin

  • The Hebrew University of Jerusalem , Jerusalem, Israel

    Leo Joskowicz

  • Harvard Medical School , Boston, USA

    Mert R. Sabuncu

  • Istanbul Technical University , Istanbul, Turkey

    Gozde Unal

  • Harvard Medical School and Brigham and Women's Hospital, Boston, USA

    William Wells

Bibliographic Information

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access