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Bayesian and grAphical Models for Biomedical Imaging

First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. M. Jorge Cardoso

    1. Centre for Medical Imaging, University College London, London, UK

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  2. Ivor Simpson

    1. Centre for Medical Imaging, University College London, London, UK

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  3. Tal Arbel

    1. Centre for Intelligent Machines, McGill University, Montreal, Canada

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  4. Doina Precup

    1. Centre for Intelligent Machines, McGill University, Montreal, Canada

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  5. Annemie Ribbens

    1. Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8677)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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Table of contents (11 papers)

  1. Front Matter

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  2. N3 Bias Field Correction Explained as a Bayesian Modeling Method

    • Christian Thode Larsen, J. Eugenio Iglesias, Koen Van Leemput
    Pages 1-12

  3. A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging

    • Chuyang Ye, Aaron Carass, Emi Murano, Maureen Stone, Jerry L. Prince
    Pages 13-24

  4. Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine

    • Florian Jug, Tobias Pietzsch, Dagmar Kainmüller, Jan Funke, Matthias Kaiser, Erik van Nimwegen et al.
    Pages 25-36

  5. Physiologically Informed Bayesian Analysis of ASL fMRI Data

    • Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda, Philippe Ciuciu, Florence Forbes
    Pages 37-48

  6. Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features

    • Neda Sepasian, Martijn Van de Giessen, Iwan Dobbe, Geert Streekstra
    Pages 49-60

  7. An Inference Language for Imaging

    • Stefano Pedemonte, Ciprian Catana, Koen Van Leemput
    Pages 61-72

  8. An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation

    • Monica Enescu, Mattias P. Heinrich, Esme Hill, Ricky Sharma, Michael A. Chappell, Julia A. Schnabel
    Pages 73-84

  9. Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model

    • Neil P. Oxtoby, Alexandra L. Young, Nick C. Fox, The Alzheimer’s Disease Neuroimaging Initiative, Pankaj Daga, David M. Cash et al.
    Pages 85-94

  10. Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can)

    • Maxime Taquet, Jurriaan M. Peters, Simon K. Warfield
    Pages 95-106

  11. Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies

    • Kayhan N. Batmanghelich, Michael Cho, Raul San Jose, Polina Golland
    Pages 107-117

  12. A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions

    • Colm Elliott, Douglas L. Arnold, D. Louis Collins, Tal Arbel
    Pages 118-129

  13. Back Matter

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Keywords

About this book

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014.
The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.

Editors and Affiliations

  • Centre for Medical Imaging, University College London, London, UK

    M. Jorge Cardoso, Ivor Simpson

  • Centre for Intelligent Machines, McGill University, Montreal, Canada

    Tal Arbel, Doina Precup

  • Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven, Belgium

    Annemie Ribbens

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