Combined pixel classification and atlas-based segmentation of the ventricular system in brain CT images

Accurate segmentation of the brain ventricular system in Computed Tomography (CT) images is useful in neurodiagnosis, providing quantitative measures on changes in ventricular size due to stroke. Manual segmentation, however, is a time-consuming, tedious task and is prone to large inter-observer variability. This study presents an automatic ventricle system segmentation method by combining the results of supervised pixel classification based on intensities with spatial information obtained from a multi-atlas-based segmentation method. The method is applied to follow-up brain CT images which were collected from a cohort of 20 patients with proven ischemic stroke. The automatic segmentation performance was evaluated in a leave-one-out strategy by comparing with manual segmentations. The results show that combining information obtained from pixel classification and multiatlas- based segmentation significantly outperforms each method independently with a mean Dice coefficient index of 0.81±0.07. © 2013 SPIE.