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Reconstruction, Segmentation, and Analysis of Medical Images

First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Maria A. Zuluaga

    1. University College London, London, United Kingdom

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  2. Kanwal Bhatia

    1. King’s College London, London, United Kingdom

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  3. Bernhard Kainz

    1. Imperial College London, London, United Kingdom

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  4. Mehdi H. Moghari

    1. Harvard Medical School and Boston Children’s Hospital, Boston, USA

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  5. Danielle F. Pace

    1. MIT, Cambridge, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10129)

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

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About this book

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016, and the First International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016.

The 17 revised regular papers presented in this book were carefully reviewed and selected from a total of 21 submissions. The papers cover following topics: Registration; Reconstruction; Deep learning for heart segmentation; Discrete optimization and probabilistic intensity modeling; Atlas-based strategies; Random forests.

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Keywords

Table of contents (17 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. RAMBO: Registration

    1. Front Matter

      Pages 1-1
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    2. Point-Spread-Function-Aware Slice-to-Volume Registration: Application to Upper Abdominal MRI Super-Resolution

      • Michael Ebner, Manil Chouhan, Premal A. Patel, David Atkinson, Zahir Amin, Samantha Read et al.
      Pages 3-13

    3. Motion Correction Using Subpixel Image Registration

      • Amir HajiRassouliha, Andrew J. Taberner, Martyn P. Nash, Poul M. F. Nielsen
      Pages 14-23

    4. Incompressible Phase Registration for Motion Estimation from Tagged Magnetic Resonance Images

      • Fangxu Xing, Jonghye Woo, Arnold D. Gomez, Dzung L. Pham, Philip V. Bayly, Maureen Stone et al.
      Pages 24-33

  3. RAMBO: Reconstruction

    1. Front Matter

      Pages 35-35
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    2. Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints

      • Cagdas Ulas, Pedro A. Gómez, Felix Krahmer, Jonathan I. Sperl, Marion I. Menzel, Bjoern H. Menze
      Pages 37-47

    3. Graph-Based 3D-Ultrasound Reconstruction of the Liver in the Presence of Respiratory Motion

      • Houssem-Eddine Gueziri, Sebastien Tremblay, Catherine Laporte, Rupert Brooks
      Pages 48-57

    4. Whole-Heart Single Breath-Hold Cardiac Cine: A Robust Motion-Compensated Compressed Sensing Reconstruction Method

      • Javier Royuela-del-Val, Muhammad Usman, Lucilio Cordero-Grande, Marcos Martin-Fernandez, Federico Simmross-Wattenberg, Claudia Prieto et al.
      Pages 58-69

    5. Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI

      • Aurélien Bustin, Anne Menini, Martin A. Janich, Darius Burschka, Jacques Felblinger, Anja C. S. Brau et al.
      Pages 70-80

  4. RAMBO and HVSMR: Deep Learning for Heart Segmentation

    1. Front Matter

      Pages 81-81
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    2. Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

      • Rudra P. K. Poudel, Pablo Lamata, Giovanni Montana
      Pages 83-94

    3. Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

      • Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum
      Pages 95-102

    4. 3D FractalNet: Dense Volumetric Segmentation for Cardiovascular MRI Volumes

      • Lequan Yu, Xin Yang, Jing Qin, Pheng-Ann Heng
      Pages 103-110

    5. Automatic Whole-Heart Segmentation in Congenital Heart Disease Using Deeply-Supervised 3D FCN

      • Jinpeng Li, Rongzhao Zhang, Lin Shi, Defeng Wang
      Pages 111-118

  5. RAMBO and HVSMR: Discrete Optimization and Probabilistic Intensity Modeling

    1. Front Matter

      Pages 119-119
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    2. A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation

      • Philipp Lösel, Vincent Heuveline
      Pages 121-128

    3. Fully-Automatic Segmentation of Cardiac Images Using 3-D MRF Model Optimization and Substructures Tracking

      • Georgios Tziritas
      Pages 129-136

  6. HSVMR: Atlas-Based Strategies

    1. Front Matter

      Pages 137-137
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    2. Strengths and Pitfalls of Whole-Heart Atlas-Based Segmentation in Congenital Heart Disease Patients

      • Maria A. Zuluaga, Benedetta Biffi, Andrew M. Taylor, Silvia Schievano, Tom Vercauteren, Sébastien Ourselin
      Pages 139-146
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Other volumes

  1. Reconstruction, Segmentation, and Analysis of Medical Images

Editors and Affiliations

  • University College London, London, United Kingdom

    Maria A. Zuluaga

  • King’s College London, London, United Kingdom

    Kanwal Bhatia

  • Imperial College London, London, United Kingdom

    Bernhard Kainz

  • Harvard Medical School and Boston Children’s Hospital, Boston, USA

    Mehdi H. Moghari

  • MIT, Cambridge, USA

    Danielle F. Pace

Bibliographic Information

  • Book Title: Reconstruction, Segmentation, and Analysis of Medical Images

  • Book Subtitle: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers

  • Editors: Maria A. Zuluaga, Kanwal Bhatia, Bernhard Kainz, Mehdi H. Moghari, Danielle F. Pace

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-52280-7

  • Publisher: Springer Cham

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

  • Copyright Information: Springer International Publishing AG 2017

  • Softcover ISBN: 978-3-319-52279-1Published: 19 January 2017

  • eBook ISBN: 978-3-319-52280-7Published: 18 January 2017

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 174

  • Number of Illustrations: 67 b/w illustrations

  • Topics: Image Processing and Computer Vision

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