Spinal Cord Segmentation for Volume Estimation in Healthy and Multiple Sclerosis Subjects Using Crawlers and Minimal Paths

Spinal cord analysis is an important problem in the study of various neurological diseases. Current segmentation and analysis methods in clinical use are slow and labor-intensive, especially for pathological data. "Spinal Crawlers" are a recently developed technique based on an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable meshes) with high-level control mechanisms. Our method extends Spinal Crawlers to better function in a clinical setting in which images of variable quality and challenging anatomy are encountered. We augment the Spinal Crawler's local optimality with that of globally optimal paths using the live-wire technique. This fusion of globally optimal paths, with locally optimal filtering allows our method to better adapt to contrast changes compared to other methods and therefore allows a larger section of the cord to be measured. Our improvements are validated on 5 vertebral levels of both healthy and pathological spinal cords from clinical MR data. This is the first study to validate a spinal cord segmentation method over a large region encompassing the length of five vertebrae.