Segmentation and identification of fatty infiltration of the erector spinae using deep learning

Paraspinal muscle degeneration, defined by changes to muscle cross-sectional area and fatty infiltration of the muscle, has been linked to the presence of low back pain, sagittal imbalance, and overall functional limitations. As a result, there is a significant clinical value in efficiently evaluating muscle degeneration. Segmentation of the paraspinal muscles is difficult due to the considerable inter- and intra- patient variability in the muscles and ambiguous boundaries between muscles. Identification of the adipose infiltration adds to this challenge due to the presence of fatty streaks within and around the muscles. In this work, we propose a method for segmenting the erector spinae autonomously and identifying the fatty infiltration into the muscle semi-autonomously. We combine segmentations from two deep U-Nets, each trained to segment on different scales. Training for both networks used manually segmented maps of the muscles on 21 axial MRIs and was validated on 10 images. Automated segmentation of the erector spinae was compared to segmentations done by an expert rater, producing an average Dice score of 0.75. Based on these segmentations, we identified the fat infiltration of the erector spinae muscle using a fuzzy c-means algorithm for generating a probability map. The accuracy of fat identification was qualitatively assessed by three independent neurological surgeons on a scale from 1 (unacceptable) to 5 (perfect). The average rating of our model was 3.87. By using this combination of supervised and unsupervised machine learning methods, we hope to quickly generate a large amount of data for fat vs. muscle segmentation in tissues of the lower back. Successful identification of fatty infiltration of the erector spinae can help us better assess paraspinal muscle degeneration and possibly uncover the etiology of low back pain.