Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis

Mark A. Horsfield, Stefania Sala, Mohit Neema, Martina Absinta, Anshika Bakshi, Maria Pia Sormani, Maria A. Rocca, Rohit Bakshi, Massimo Filippi

Research output: Contribution to journalArticlepeer-review

188 Scopus citations


A new semi-automatic method for segmenting the spinal cord from MR images is presented. The method is based on an active surface (AS) model of the cord surface, with intrinsic smoothness constraints. The model is initialized by the user marking the approximate cord center-line on a few representative slices, and the compact surface parametrization results in a rapid segmentation, taking on the order of 1 min. Using 3-D acquired T1-weighted images of the cervical spine from human controls and patients with multiple sclerosis, the intra- and inter-observer reproducibilities were evaluated, and compared favorably with an existing cord segmentation method. While the AS method overestimated the cord area by approximately 14% compared to manual outlining, correlations between cord cross-sectional area and clinical disability scores confirmed the relevance of the new method in measuring cord atrophy in multiple sclerosis. Segmentation of the cord from 2-D multi-slice T2-weighted images is also demonstrated over the cervical and thoracic region. Since the cord center-line is an intrinsic parameter extracted as part of the segmentation process, the image can be resampled such that the center-line forms one coordinate axis of a new image, allowing simple visualization of the cord structure and pathology; this could find wider application in standard radiological practice.

Original languageEnglish (US)
Pages (from-to)446-455
Number of pages10
Issue number2
StatePublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


Dive into the research topics of 'Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis'. Together they form a unique fingerprint.

Cite this