IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI

  • Nagesh SubbannaEmail author
  • Doina Precup
  • Douglas Arnold
  • Tal Arbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)


In this paper, we present IMaGe, a new, iterative two-stage probabilistic graphical model for detection and segmentation of Multiple Sclerosis (MS) lesions. Our model includes two levels of Markov Random Fields (MRFs). At the bottom level, a regular grid voxel-based MRF identifies potential lesion voxels, as well as other tissue classes, using local and neighbourhood intensities and class priors. Contiguous voxels of a particular tissue type are grouped into regions. A higher, non-lattice MRF is then constructed, in which each node corresponds to a region, and edges are defined based on neighbourhood relationships between regions. The goal of this MRF is to evaluate the probability of candidate lesions, based on group intensity, texture and neighbouring regions. The inferred information is then propagated to the voxel-level MRF. This process of iterative inference between the two levels repeats as long as desired. The iterations suppress false positives and refine lesion boundaries. The framework is trained on 660 MRI volumes of MS patients enrolled in clinical trials from 174 different centres, and tested on a separate multi-centre clinical trial data set with 535 MRI volumes. All data consists of T1, T2, PD and FLAIR contrasts. In comparison to other MRF methods, such as [5, 9], and a traditional MRF, IMaGe is much more sensitive (with slightly better PPV). It outperforms its nearest competitor by around 20 % when detecting very small lesions (3–10 voxels). This is a significant result, as such lesions constitute around 40 % of the total number of lesions.


Multiple Sclerosis Positive Predictive Value Multiple Sclerosis Patient Class Label Multiple Sclerosis Lesion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nagesh Subbanna
    • 1
    Email author
  • Doina Precup
    • 1
  • Douglas Arnold
    • 1
  • Tal Arbel
    • 1
  1. 1.McGill UniversityMontrealCanada

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