Segmenting and quantifying @entity11 from MRI is an important task for diagnosis, planning intervention, and for tracking @entity5 changes over time. However, this task is complicated by the lack of prior knowledge concerning @entity5 location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and @entity5 segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package-a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal @entity1313 Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade @entity5 four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" @entity5 components, respectively.

Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised XXXX Segmentation (Simplified) with ANTsR.

@entity11 :: ('MESH:D005910', 'Disease') :: ['gliomas'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'glioma tumor'] @entity1313 :: ('MESH:D001932', 'Disease') :: ['Brain Tumor']
@entity1313 :: (MESH:D001932,Disease) :: ['Brain Tumor']
