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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Dinggang Shen

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  2. Tianming Liu

    1. University of Georgia, Athens, USA

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  3. Terry M. Peters

    1. Western University, London, Canada

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  4. Lawrence H. Staib

    1. Yale University, New Haven, USA

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  5. Caroline Essert

    1. University of Strasbourg, Illkirch, France

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  6. Sean Zhou

    1. United Imaging Intelligence, Shanghai, China

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  7. Pew-Thian Yap

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  8. Ali Khan

    1. Western University, London, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11767)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MICCAI 2019.

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Table of contents (87 papers)

  1. Front Matter

    Pages i-xxxviii
    Download chapter PDF

  2. Shape (Including Neuroimage Shape)

    1. Front Matter

      Pages 1-1
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    2. A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations

      • Philipp Ernst, Georg Hille, Christian Hansen, Klaus Tönnies, Marko Rak
      Pages 3-11

    3. Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity

      • Pan Su, Yitian Zhao, Tianhua Chen, Jianyang Xie, Yifan Zhao, Hong Qi et al.
      Pages 12-20

    4. A Surface-Theoretic Approach for Statistical Shape Modeling

      • Felix Ambellan, Stefan Zachow, Christoph von Tycowicz
      Pages 21-29

    5. One-Stage Shape Instantiation from a Single 2D Image to 3D Point Cloud

      • Xiao-Yun Zhou, Zhao-Yang Wang, Peichao Li, Jian-Qing Zheng, Guang-Zhong Yang
      Pages 30-38

    6. Placental Flattening via Volumetric Parameterization

      • S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland
      Pages 39-47

    7. Fast Polynomial Approximation to Heat Diffusion in Manifolds

      • Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo K. Chung
      Pages 48-56

    8. Hierarchical Multi-geodesic Model for Longitudinal Analysis of Temporal Trajectories of Anatomical Shape and Covariates

      • Sungmin Hong, James Fishbaugh, Jason J. Wolff, Martin A. Styner, Guido Gerig, the IBIS Network
      Pages 57-65

    9. Clustering of Longitudinal Shape Data Sets Using Mixture of Separate or Branching Trajectories

      • Vianney Debavelaere, Alexandre Bône, Stanley Durrleman, Stéphanie Allassonnière, for the Alzheimer’s Disease Neuroimaging Initiative
      Pages 66-74

    10. Group-Wise Graph Matching of Cortical Gyral Hinges

      • Tuo Zhang, Xiao Li, Lin Zhao, Ying Huang, Zhibin He, Lei Guo et al.
      Pages 75-83

    11. Multi-view Graph Matching of Cortical Landmarks

      • Ying Huang, Zhibin He, Lei Guo, Tianming Liu, Tuo Zhang
      Pages 84-92

    12. Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

      • Bernhard Egger, Markus D. Schirmer, Florian Dubost, Marco J. Nardin, Natalia S. Rost, Polina Golland
      Pages 93-101

    13. Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance

      • Kristen M. Campbell, Jeffrey S. Anderson, P. Thomas Fletcher
      Pages 102-110

  3. Prediction

    1. Front Matter

      Pages 111-111
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    2. Diagnosis-Guided Multi-modal Feature Selection for Prognosis Prediction of Lung Squamous Cell Carcinoma

      • Wei Shao, Tongxin Wang, Zhi Huang, Jun Cheng, Zhi Han, Daoqiang Zhang et al.
      Pages 113-121

    3. Graph Convolution Based Attention Model for Personalized Disease Prediction

      • Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Benedict Wiestler et al.
      Pages 122-130

    4. Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-Rank Feature Learning

      • Haijun Lei, Yujia Zhao, Baiying Lei
      Pages 131-139

    5. Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions

      • Lyujian Lu, Saad Elbeleidy, Lauren Baker, Hua Wang, Heng Huang, Li Shen et al.
      Pages 140-148

    6. Deep Granular Feature-Label Distribution Learning for Neuroimaging-Based Infant Age Prediction

      • Dan Hu, Han Zhang, Zhengwang Wu, Weili Lin, Gang Li, Dinggang Shen et al.
      Pages 149-157
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  7. Computational Biomechanics for Medicine

  8. Computational Diffusion MRI

Keywords

About this book

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.

The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: optical imaging; endoscopy; microscopy.

Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.

Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.

Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.

Part V: computer assisted interventions; MIC meets CAI.

Part VI: computed tomography; X-ray imaging.

Editors and Affiliations

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Dinggang Shen, Pew-Thian Yap

  • University of Georgia, Athens, USA

    Tianming Liu

  • Western University, London, Canada

    Terry M. Peters, Ali Khan

  • Yale University, New Haven, USA

    Lawrence H. Staib

  • University of Strasbourg, Illkirch, France

    Caroline Essert

  • United Imaging Intelligence, Shanghai, China

    Sean Zhou

Bibliographic Information

  • Book Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  • Book Subtitle: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV

  • Editors: Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, … Ali Khan

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-32251-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-32250-2Published: 18 October 2019

  • eBook ISBN: 978-3-030-32251-9Published: 10 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXXVIII, 809

  • Topics: Image Processing and Computer Vision, Pattern Recognition, Artificial Intelligence, Health Informatics

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