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Medical Computer Vision: Algorithms for Big Data

International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Bjoern Menze

    1. Technische Universität München, München, 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, Sierre, Switzerland

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  6. Shaoting Zhang

    1. University of North Carolina, Charlotte, USA

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  7. Weidong (Tom) Cai

    1. School of Information Technologies, Biomedical & Multimedia Info. Tech.(BMIT) Research Group, University of Sydney, Sydney, Australia

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  8. Dimitris Metaxas

    1. Rutgers University, Piscataway, USA

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  • Fast track conference proceedings
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  • Includes supplementary material: sn.pub/extras

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8848)

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 2014.

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About this book

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and "big data" algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission.

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Keywords

Table of contents (19 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. Workshop Overview

    1. Front Matter

      Pages 1-1
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    2. Overview of the 2014 Workshop on Medical Computer Vision—Algorithms for Big Data (MCV 2014)

      • Henning Müller, Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Shaoting Zhang et al.
      Pages 3-10

  3. Segmentation of Big Medical Data

    1. Front Matter

      Pages 11-11
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    2. Joint Segmentation and Registration for Infant Brain Images

      • Guorong Wu, Li Wang, John Gilmore, Weili Lin, Dinggang Shen
      Pages 13-21

    3. LINKS: Learning-Based Multi-source IntegratioN FrameworK for Segmentation of Infant Brain Images

      • Li Wang, Yaozong Gao, Feng Shi, Gang Li, John H. Gilmore, Weili Lin et al.
      Pages 22-33

    4. Pectoralis Muscle Segmentation on CT Images Based on Bayesian Graph Cuts with a Subject-Tailored Atlas

      • Rola Harmouche, James C. Ross, George R. Washko, Raúl San José Estépar
      Pages 34-44

  4. Advanced Feature Extraction

    1. Front Matter

      Pages 45-45
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    2. Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine

      • Gijs van Tulder, Marleen de Bruijne
      Pages 47-58

    3. Dementia-Related Features in Longitudinal MRI: Tracking Keypoints over Time

      • Elisabeth Stühler, Michael R. Berthold
      Pages 59-70

    4. Object Classification in an Ultrasound Video Using LP-SIFT Features

      • Mohammad Ali Maraci, Raffaele Napolitano, Aris Papageorghiou, J. Allison Noble
      Pages 71-81

    5. Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification

      • Thomas Schlegl, Joachim Ofner, Georg Langs
      Pages 82-93

  5. Multi-atlas and Beyond

    1. Front Matter

      Pages 95-95
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    2. Atlas-Guided Multi-channel Forest Learning for Human Brain Labeling

      • Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu, Dinggang Shen
      Pages 97-104

    3. Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning

      • David Rivest-Hénault, Soumya Ghose, Josien P. W. Pluim, Peter B. Greer, Jurgen Fripp, Jason A. Dowling
      Pages 105-115

    4. Classifier-Based Multi-atlas Label Propagation with Test-Specific Atlas Weighting for Correspondence-Free Scenarios

      • Darko Zikic, Ben Glocker, Antonio Criminisi
      Pages 116-124

  6. Translational Medical Computer Vision

    1. Front Matter

      Pages 125-125
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    2. CT Prostate Deformable Segmentation by Boundary Regression

      • Yeqin Shao, Yaozong Gao, Xin Yang, Dinggang Shen
      Pages 127-136

    3. Precise Lumen Segmentation in Coronary Computed Tomography Angiography

      • Felix Lugauer, Yefeng Zheng, Joachim Hornegger, B. Michael Kelm
      Pages 137-147

    4. Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets

      • Rocío Cabrera-Lozoya, Jan Margeta, Loïc Le Folgoc, Yuki Komatsu, Benjamin Berte, Jatin Relan et al.
      Pages 148-159
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Other volumes

  1. Medical Computer Vision: Algorithms for Big Data

Editors and Affiliations

  • Technische Universität München, München, 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, Sierre, Switzerland

    Henning Müller

  • University of North Carolina, Charlotte, USA

    Shaoting Zhang

  • School of Information Technologies, Biomedical & Multimedia Info. Tech.(BMIT) Research Group, University of Sydney, Sydney, Australia

    Weidong (Tom) Cai

  • Rutgers University, Piscataway, USA

    Dimitris Metaxas

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