Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

ABSTRACT: The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability

SUBMITTER: Gros C 

PROVIDER: S-EPMC6759925 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature