Paper
20 March 2015 Highly accurate volumetry of the spinal cord
Florian Weiler, Marita Daams, Carsten Lukas, Frederik Barkhof, Horst K. Hahn
Author Affiliations +
Abstract
Quantitative analysis of the spinal cord from MR images is of significant clinical interest when studying certain neurologic diseases. Especially for multiple sclerosis, a number of studies have analyzed the relation between spinal cord atrophy and clinically monitored progression of the disease. A commonly analyzed parameter in this field is the mean cross-sectional area of the cord, which can also be expressed as the average volume per cm. In this paper, we present a novel approach for precise measurement of the volume, length, and cross-sectional area of the spinal cord from T1-weighted MR images. It is computationally fast, with a low effort of required user interaction. It is based on a semi-automated pre-segmentation of a sub-section of the spinal cord, followed by an automated Gaussian mixture-model fit for volume calculation. Additionally, the centerline of the cord is extracted, which allows for calculation of the mean cross-sectional area of the measured section. We evaluate the accuracy of our method with respect to scan/re-scan reproducibility as well as intra- and inter-rater agreement. We achieved a mean coefficient of variation of 0.62% over repeated MR acquisitions, mean CoV of 0.39% for intra-rater comparison, and a mean CoV of 0.28% for inter-rater comparison by five different observers. These results prove a high sensitivity to detect even small changes in atrophy, as it could typically be observed over the temporal progression of MS
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Florian Weiler, Marita Daams, Carsten Lukas, Frederik Barkhof, and Horst K. Hahn "Highly accurate volumetry of the spinal cord", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941302 (20 March 2015); https://doi.org/10.1117/12.2080803
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Cited by 5 scholarly publications.
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KEYWORDS
Spinal cord

Magnetic resonance imaging

Tissues

Image segmentation

Photovoltaics

3D image processing

Data modeling

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