Spinal cord gray matter segmentation using deep dilated convolutions

TL;DR

This paper presents a deep learning-based method for automatic segmentation of spinal cord gray matter in MRI images, achieving state-of-the-art results and reducing network complexity.

Contribution

It introduces a simple, end-to-end deep learning approach that outperforms existing methods in GM segmentation for both in vivo and ex vivo MRI data.

Findings

Achieved state-of-the-art performance on GM segmentation challenge

Reduced network parameters compared to traditional architectures

Performed well on both in vivo and ex vivo MRI data

Abstract

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also 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 and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.