Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images

Pavlina Sonkova; Iordanis E. Evangelou; Antonio Gallo; Fredric K Cantor; Joan Ohayon; Henry F. McFarland; Francesca Bagnato

doi:10.1117/12.773055

11 March 2008 Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images

Pavlina Sonkova, Iordanis E. Evangelou, Antonio Gallo, Fredric K Cantor, Joan Ohayon, Henry F. McFarland, Francesca Bagnato

Author Affiliations +

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69144I (2008) https://doi.org/10.1117/12.773055
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Spinal cord (SC) tissue loss is known to occur in some patients with multiple sclerosis (MS), resulting in SC atrophy. Currently, no measurement tools exist to determine the magnitude of SC atrophy from Magnetic Resonance Images (MRI). We have developed and implemented a novel semi-automatic method for quantifying the cervical SC volume (CSCV) from Magnetic Resonance Images (MRI) based on level sets. The image dataset consisted of SC MRI exams obtained at 1.5 Tesla from 12 MS patients (10 relapsing-remitting and 2 secondary progressive) and 12 age- and gender-matched healthy volunteers (HVs). 3D high resolution image data were acquired using an IR-FSPGR sequence acquired in the sagittal plane. The mid-sagittal slice (MSS) was automatically located based on the entropy calculation for each of the consecutive sagittal slices. The image data were then pre-processed by 3D anisotropic diffusion filtering for noise reduction and edge enhancement before segmentation with a level set formulation which did not require re-initialization. The developed method was tested against manual segmentation (considered ground truth) and intra-observer and inter-observer variability were evaluated.

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Pavlina Sonkova, Iordanis E. Evangelou, Antonio Gallo, Fredric K Cantor, Joan Ohayon, Henry F. McFarland, and Francesca Bagnato "Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69144I (11 March 2008); https://doi.org/10.1117/12.773055

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