Three-dimensional segmentation of MR images of the head using probability and connectivity

Abstract

We describe a three-dimensional (3D) segmentation method that comprises (a) user interactive identification of tissue classes; (b) calculation of a probability distribution for each tissue; (c) creation of a feature map of the most probable tissues; (d) 3D segmentation of the magnetic resonance (MR) data; (e) smoothing of the segmented data; (f) extraction of surfaces of interest with connectivity; (g) generation of surfaces; and (h) rendering of multiple surfaces to plan surgery. Patients with normal head anatomy and with abnormalities such as multiple sclerosis lesions and brain tumors were scanned with a 1.5 T MR system using a two echo contiguous (interleaved), multislice pulse sequence that provides both proton density and T2-weighted contrast. After the user identified the tissues, the 3D data were automatically segmented into background, facial tissue, brain matter, CSF, and lesions. Surfaces of the face, brain, lateral ventricles, tumors, and multiple sclerosis lesions are displayed using color coding and gradient shading. Color improves the visualization of segmented tissues, while gradient shading enhances the perception of depth. Manipulation of the 3D model on a workstation aids surgical planning. Sulci and gyri stand out, thus aiding functional mapping of the brain surface.