Abstract
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.
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Notes
- 1.
Partial volume denotes the class ascribed to voxels which are a mix of GM and CSF. This class is created in order to reduce the number of false negatives at the edges of the ventricles.
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Subbanna, N., Precup, D., Arnold, D., Arbel, T. (2015). IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_40
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