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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Nassir Navab

    1. TU München, Garching, Germany

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  2. Joachim Hornegger

    1. Lehrstuhl Informatik 5, University of Erlangen-Nuremberg, Erlangen, Germany

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  3. William M. Wells

    1. Medical School, Brigham & Women’s Hospital Harvard, Boston, USA

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  4. Alejandro F. Frangi

    1. Electronic & Electrical Eng, University of Sheffield, Sheffield, United Kingdom

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9351)

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 2015.

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Table of contents (91 papers)

  1. Front Matter

    Pages I-XXVII
    Download chapter PDF

  2. Quantitative Image Analysis I: Segmentation and Measurement

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

      • Tom Brosch, Youngjin Yoo, Lisa Y. W. Tang, David K. B. Li, Anthony Traboulsee, Roger Tam
      Pages 3-11

    3. Unsupervised Myocardial Segmentation for Cardiac MRI

      • Anirban Mukhopadhyay, Ilkay Oksuz, Marco Bevilacqua, Rohan Dharmakumar, Sotirios A. Tsaftaris
      Pages 12-20

    4. Multimodal Cortical Parcellation Based on Anatomical and Functional Brain Connectivity

      • Chendi Wang, Burak Yoldemir, Rafeef Abugharbieh
      Pages 21-28

    5. Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning

      • Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Anna L. David, Jan Deprest et al.
      Pages 29-37

    6. GPSSI: Gaussian Process for Sampling Segmentations of Images

      • Matthieu Lê, Jan Unkelbach, Nicholas Ayache, Hervé Delingette
      Pages 38-46

    7. Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRI

      • Salim Arslan, Daniel Rueckert
      Pages 47-54

    8. Interactive Multi-organ Segmentation Based on Multiple Template Deformation

      • Romane Gauriau, David Lesage, Mélanie Chiaradia, Baptiste Morel, Isabelle Bloch
      Pages 55-62

    9. Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longitudinal Data

      • Yanrong Guo, Guorong Wu, Pew-Thian Yap, Valerie Jewells, Weili Lin, Dinggang Shen
      Pages 63-71

    10. Measuring Cortical Neurite-Dispersion and Perfusion in Preterm-Born Adolescents Using Multi-modal MRI

      • Andrew Melbourne, Zach Eaton-Rosen, David Owen, Jorge Cardoso, Joanne Beckmann, David Atkinson et al.
      Pages 72-79

    11. Interactive Whole-Heart Segmentation in Congenital Heart Disease

      • Danielle F. Pace, Adrian V. Dalca, Tal Geva, Andrew J. Powell, Mehdi H. Moghari, Polina Golland
      Pages 80-88

    12. Automatic 3D US Brain Ventricle Segmentation in Pre-Term Neonates Using Multi-phase Geodesic Level-Sets with Shape Prior

      • Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen, Martin Rajchl, Eranga Ukwatta et al.
      Pages 89-96

    13. Multiple Surface Segmentation Using Truncated Convex Priors

      • Abhay Shah, Junjie Bai, Zhihong Hu, Srinivas Sadda, Xiaodong Wu
      Pages 97-104

    14. Statistical Power in Image Segmentation: Relating Sample Size to Reference Standard Quality

      • Eli Gibson, Henkjan J. Huisman, Dean C. Barratt
      Pages 105-113

    15. Joint Learning of Image Regressor and Classifier for Deformable Segmentation of CT Pelvic Organs

      • Yaozong Gao, Jun Lian, Dinggang Shen
      Pages 114-122

    16. Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsic and Intrinsic Image Cues

      • Lisa Y. W. Tang, Ghassan Hamarneh, Anthony Traboulsee, David Li, Roger Tam
      Pages 123-131

    17. Brain Tissue Segmentation Based on Diffusion MRI Using ℓ0 Sparse-Group Representation Classification

      • Pew-Thian Yap, Yong Zhang, Dinggang Shen
      Pages 132-139

    18. A Latent Source Model for Patch-Based Image Segmentation

      • George H. Chen, Devavrat Shah, Polina Golland
      Pages 140-148

    19. Multi-organ Segmentation Using Shape Model Guided Local Phase Analysis

      • Chunliang Wang, Örjan Smedby
      Pages 149-156
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Other volumes

  1. Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

  2. Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

  3. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

Keywords

About this book

The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions. 

Editors and Affiliations

  • TU München, Garching, Germany

    Nassir Navab

  • Lehrstuhl Informatik 5, University of Erlangen-Nuremberg, Erlangen, Germany

    Joachim Hornegger

  • Medical School, Brigham & Women’s Hospital Harvard, Boston, USA

    William M. Wells

  • Electronic & Electrical Eng, University of Sheffield, Sheffield, United Kingdom

    Alejandro F. Frangi

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