[BIC-announce] Special Issue on Probabilistic Models for Biomedical Image Analysis

[Sylvain Baillet, Dr]

On behalf of Dr. Tal Arbel (McGill's Centre for Intelligent Machines)
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Elsevier Journal of Computer Vision and Image Understanding (CVIU)
Special Issue on Probabilistic Models for Biomedical Image Analysis
Website: http://www.journals.elsevier.com/computer-vision-and-image-understanding/call-for-papers/probabilistic-models-for-biomedical-image-analysis/
[Special          Issue on Probabilistic Models for Biomedical Image Analysis]Scope
Probabilistic models have been developed for a wide variety of contexts in the field of computer vision. Despite their demonstrable power, there are still many significant questions to consider regarding their effective exploitation in the analysis of biomedical images. For example, objective selection of different models or estimates of parameter uncertainty may facilitate improved interpretability or plausibility. Also, improvements in the efficiency of inference techniques may allow hierarchical, or higher-order models to be tractably inferred upon, reducing the dependence on subjective modelling choices. This special issue will bridge the gap between researchers in computer vision, biomedical image analysis and machine learning by providing a platform for the exploration of probabilistic modeling approaches for difficult clinical problems within a variety of biomedical imaging contexts.
Academic Objectives
For this special issue, we are looking for original, innovative and mathematically rigorous Bayesian/probabilistic models and inference algorithms that demonstrate clear advantages in terms of accuracy or interpretability over other state-of-art methods in the analysis of medical imaging data. The goal is to encourage novel explorative contributions that may be of a more theoretical nature than those in mainstream medical imaging journals, but are essential for the advancement of modeling and analysis of biomedical imaging data.
Topics
Authors are invited to submit original research papers and high-quality overview and survey articles on topics including, but not limited to:
Methodology

  *   Biologically/physically plausible and realistic generative models,
  *   Model comparison and averaging,
  *   Model uncertainty,
  *   Modelling of multi-modal data,
  *   Probabilistic graphical models,
  *   Efficient inference strategies.
Applications
Potential applications cover the full scope of biomedical image analysis:

  *   Image segmentation/parcellation,
  *   Image registration/data fusion,
  *   Image reconstruction,
  *   Atlas construction,
  *   Pathological classification,
  *   Longitudinal population analysis,
  *   Cross-sectional population analysis,
  *   Tractography and microstructural modeling,
  *   Disease modelling,
  *   Imaging Genomics,
  *   Functional image modeling.
Paper Submission
Full papers can be submitted via the online submission system for CVIU (http://ees.elsevier.com/cviu/). Authors must select “SI: Probabilistic Models” as “Article Type” in the submission process. Preparation of the manuscript must follow the Guide for Authors, which is available here<http://www.elsevier.com/journals/computer-vision-and-image-understanding/1077-3142/guide-for-authors>.
Dates

  *   Submission Deadline: January 15, 2015
  *   First Round Decisions: June 15, 2015
  *   Revisions Deadline: Sept 15, 2015
  *   Final Round Decisions: Dec 15, 2015
  *   Online Publication: Jan 2016
Lead Guest Editor
Tal Arbel
arbel at cim.mcgill.ca<mailto:arbel at cim.mcgill.ca>
Centre for Intelligent Machines, McGill University

Guest Editors
M. Jorge Cardoso
m.jorge.cardoso at ucl.ac.uk<mailto:m.jorge.cardoso at ucl.ac.uk>
Centre for Medical Image Computing, University College London

Albert C. S. Chung
achung at cse.ust.hk<mailto:achung at cse.ust.hk>
The Hong Kong University of Science and Technology
Doina Precup
dprecup at cs.mcgill.ca<mailto:dprecup at cs.mcgill.ca>
School of Computer Science, McGill University

William Wells III
sw at bwh.harvard.edu<mailto:sw at bwh.harvard.edu>
Harvard Medical School and Brigham and Women's Hospital, M.I.T.

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