Detecting mild traumatic brain injury using dynamic low level context

Mild traumatic brain injury is difficult to detect in standard magnetic resonance (MR) images due to the low contrast appearance of lesions. In this paper a discriminative approach is presented, using a classifier to directly estimates the posterior probability of lesion at every voxel using low-level context learned from previous classifiers. Both visual features including multiple texture measures, and context features, which include novel features such as proximity, directional distance, and posterior marginal edge distance, are used. The context is also taken from previous time points, so the system automatically captures the dynamics of the injury progression. The approach is tested on an mTBI rat model using MR imaging at multiple time points. Our results show an improved performance in both the dice score and convergence rate compared to other approaches.