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Sci Rep. 2018 Apr 13;8(1):5966. doi: 10.1038/s41598-018-24304-3.

Spinal cord gray matter segmentation using deep dilated convolutions.

Author information

1
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada.
2
Duke University Medical Center, Department of Radiology, Center for In Vivo Microscopy, Durham, NC, 27710, USA.
3
University of California San Francisco, Department of Radiology & Biomedical Imaging, San Francisco, CA, 94143, USA.
4
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada. jcohen@polymtl.ca.
5
Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, H3C 3J7, Canada. jcohen@polymtl.ca.

Abstract

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.

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