Variational segmentation of the white and gray matter in the spinal cord using a shape prior

Abstract

Segmenting the inner structure of the spinal cord on magnetic resonance (MR) images is difficult because of poor contrast between white and gray matter (WM/GM). We present a variational formulation to automatically detect cerebrospinal fluid and WM/GM. The segmentation results are obtained by continuous cuts combined with a shape prior. Intensity-based segmentation guarantees high accuracy while the shape prior aims at precision. We tested the algorithm on a set of MR images with visual WM/GM contrast and evaluated it w.r.t. manual GM segmentations. The automated GM segmentations are on a par with the manual results.