Multi-atlas Parcellation in the Presence of Lesions: Application to Multiple Sclerosis

Intensity-based multi-atlas strategies have shown leading performance in segmenting healthy subjects, but when lesions are present, the abnormal lesion intensities affect the fusion result. Here, we propose a reformulated statistical fusion approach for multi-atlas segmentation that is applicable to both healthy and injured brains. This method avoids the interference of lesion intensities on the segmentation by incorporating two a priori masks to the Non-Local STAPLE statistical framework. First, we extend the theory to include a lesion mask, which improves the voxel correspondence between the target and the atlases. Second, we extend the theory to include a known label mask, that forces the label decision in case it is beforehand known and enables seamless integration of manual edits. We evaluate our method with simulated and MS patient images and compare our results with those of other state-of-the-art multi-atlas strategies: Majority vote, Non-local STAPLE, Non-local Spatial STAPLE and Joint Label Fusion. Quantitative and qualitative results demonstrate the improvement in the lesion areas.