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Medical Computer Vision. Large Data in Medical Imaging

Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Bjoern Menze

    1. Technical University of Munich, ETH, Zürich, Switzerland, Munich, Germany

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  2. Georg Langs

    1. Medical University of Vienna, Vienna, Austria

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  3. Albert Montillo

    1. GE Global Research, Niskayuna, USA

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  4. Michael Kelm

    1. Siemens AG, Erlangen, Germany

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  5. Henning Müller

    1. University of Applied Sciences Western Switzerland, Sierre, Switzerland

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  6. Zhuowen Tu

    1. University of California, La Jolla, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8331)

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

Included in the following conference series:

Conference proceedings info: MCV 2013.

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Table of contents (22 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. Workshop Overview

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Overview of the 2013 Workshop on Medical Computer Vision (MCV 2013)

      • Henning Müller, Bjoern H. Menze, Georg Langs, Albert Montillo, B. Michael Kelm, Zhuowen Tu et al.
      Pages 3-10

  3. Registration and Visualization

    1. Front Matter

      Pages 11-11
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    2. Semi-supervised Learning of Nonrigid Deformations for Image Registration

      • John A. Onofrey, Lawrence H. Staib, Xenophon Papademetris
      Pages 13-23

    3. Local Regression Learning via Forest Classification for 2D/3D Deformable Registration

      • Chen-Rui Chou, Stephen Pizer
      Pages 24-33

    4. Flexible Architecture for Streaming and Visualization of Large Virtual Microscopy Images

      • Germán Corredor, Marcela Iregui, Viviana Arias, Eduardo Romero
      Pages 34-43

    5. 2D-PCA Shape Models: Application to 3D Reconstruction of the Human Teeth from a Single Image

      • Aly S. Abdelrehim, Aly A. Farag, Ahmed M. Shalaby, Moumen T. El-Melegy
      Pages 44-52

    6. Class-Specific Regression Random Forest for Accurate Extraction of Standard Planes from 3D Echocardiography

      • Kiryl Chykeyuk, Mohammad Yaqub, J. Alison Noble
      Pages 53-62

  4. Segmentation

    1. Front Matter

      Pages 63-63
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    2. Accurate Whole-Brain Segmentation for Alzheimer’s Disease Combining an Adaptive Statistical Atlas and Multi-atlas

      • Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N. Metaxas, Albert Montillo, The Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
      Pages 65-73

    3. Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies

      • Stefan Bauer, Jean Tessier, Oliver Krieter, Lutz-P. Nolte, Mauricio Reyes
      Pages 74-83

    4. Robust Mixture-Parameter Estimation for Unsupervised Segmentation of Brain MR Images

      • Alfiia Galimzianova, Žiga Špiclin, Boštjan Likar, Franjo Pernuš
      Pages 84-94

    5. White Matter Supervoxel Segmentation by Axial DP-Means Clustering

      • Ryan P. Cabeen, David H. Laidlaw
      Pages 95-104

    6. Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

      • Quan Wang, Dijia Wu, Le Lu, Meizhu Liu, Kim L. Boyer, Shaohua Kevin Zhou
      Pages 105-115

  5. Detection and Localization

    1. Front Matter

      Pages 117-117
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    2. Local Phase-Based Fast Ray Features for Automatic Left Ventricle Apical View Detection in 3D Echocardiography

      • João S. Domingos, Eduardo Lima, Paul Leeson, J. Alison Noble
      Pages 119-129

    3. Automatic Aorta Detection in Non-contrast 3D Cardiac CT Images Using Bayesian Tracking Method

      • Mingna Zheng, J. Jeffery Carr, Yaorong Ge
      Pages 130-137

    4. Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models

      • Qi Song, Albert Montillo, Roshni Bhagalia, V. Srikrishnan
      Pages 138-147

    5. Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography

      • Florin C. Ghesu, Michael Wels, Anna Jerebko, Michael Sühling, Joachim Hornegger, B. Michael Kelm
      Pages 148-157
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  1. Medical Computer Vision. Large Data in Medical Imaging

Keywords

About this book

This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Medical Computer Vision, MCV 2013, held in Nagoya, Japan, in September 2013 in conjunction with the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013. The 7 revised full papers and 12 poster papers presented were selected from 25 submissions. They have been organized in topical sections on registration and visualization, segmentation, detection and localization, and features and retrieval. In addition, the volume contains two invited papers describing segmentation task and data set of the VISCERAL benchmark challenge.

Editors and Affiliations

  • Technical University of Munich, ETH, Zürich, Switzerland, Munich, Germany

    Bjoern Menze

  • Medical University of Vienna, Vienna, Austria

    Georg Langs

  • GE Global Research, Niskayuna, USA

    Albert Montillo

  • Siemens AG, Erlangen, Germany

    Michael Kelm

  • University of Applied Sciences Western Switzerland, Sierre, Switzerland

    Henning Müller

  • University of California, La Jolla, USA

    Zhuowen Tu

Bibliographic Information

  • Book Title: Medical Computer Vision. Large Data in Medical Imaging

  • Book Subtitle: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers

  • Editors: Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Müller, Zhuowen Tu

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-05530-5

  • Publisher: Springer Cham

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

  • Copyright Information: Springer International Publishing Switzerland 2014

  • Softcover ISBN: 978-3-319-05529-9Published: 10 April 2014

  • eBook ISBN: 978-3-319-05530-5Published: 31 March 2014

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 229

  • Number of Illustrations: 93 b/w illustrations

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

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