Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework

Traumatic Brain Injury (TBI) occurs when a sudden injury causes trauma to the brain. Contusions are one of the most common types of lesion that arise after TBI, and they can be observed on a subject's MRI or CT. Since it is hypothesised that indices such as contusion load may be potential biomarkers for TBI, the ability to segment contusions is highly desirable. Currently, we are not aware of any fully automated methods that address this segmentation task. In this paper we present a completely automated random-forest based approach to contusion segmentation that uses multi-modality MRI. Given a training set of MR images and ground-truth segmentations, a set of features is derived for each voxel that describe both the local neighbourhood and longer-range contextual information in the images. A random forest is trained using these features and the ground-truth voxel labels, and used to produce an automatic contusion segmentation of an unseen test subject. We evaluate the method using 6-fold cross-validation on a dataset consisting of 23 subjects, obtaining a mean DICE overlap of 0.60.