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.
KEYWORDS: Scanners, Image segmentation, Data acquisition, Magnetic resonance imaging, Evolutionary algorithms, Medical imaging, Image processing algorithms and systems, Data modeling, Artificial intelligence, Medicine
Machine learning algorithms tend to perform better within the setting wherein they are trained, a phenomenon known as the domain effect. Deep learning-based medical image segmentation algorithms are often trained using data acquired from specific scanners; however, these algorithms are expected to accurately segment anatomy in images acquired from scanners different from the ones used to obtain training images for such algorithms. In this work, we present evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images. The trained network performs better on new data from the same scanner and worse on data from other scanners, demonstrating a scanner-specific domain effect. We then construct ensembles of the U-Nets, in which each U-Net in the ensemble differs from others only in initialization. Finally, we demonstrate that these UNet ensembles reduce the differential between in-domain and out-of-domain performance, thereby mitigating the domain effect associated with single U-Nets. Our study evidences the importance of developing software robust to scanner-specific domain effects to handle scanner bias in Deep Learning.
KEYWORDS: Image segmentation, Spine, Spinal cord, Magnetic resonance imaging, Medical imaging, Pathology, Machine learning, Medical research, Picture Archiving and Communication System
The automated interpretation of spinal imaging using machine learning has emerged as a promising method for standardizing the assessment and diagnosis of numerous spinal column pathologies. While magnetic resonance images (MRIs) of the lumbar spine have been extensively studied in this context, the cervical spine remains vastly understudied. Our objective was to develop a method for automatically delineating cervical spinal cord and neural foramina on axial MRIs using machine learning. In this study, we train a state-of-the-art algorithm, namely a multiresolution ensemble of deep U-Nets, to delineate cervical spinal cord and neural foramina on 50 axial T2-weighted MRI-series segmented by a team of expert clinicians. We then evaluate algorithm performance against two independent human raters using 50 separate MRI-series. Dice coefficients, Hausdorff coefficients, and average surface distances (ASDs) were computed for this final set between the algorithm and each rater, and between raters, in order to evaluate algorithm performance for each segmentation task. The resulting cervical cord Dice coefficients were 0.76 (auto vs human, average) and 0.87 (human vs human), and the cervical foramina Dice coefficients were 0.57 (auto vs human, average) and 0.59 (human vs human). Hausdorff coefficients and ASDs reflected similar results. We conclude that the algorithm achieved a higher degree of consistency with human raters for cervical cord than for cervical foramina, and that cervical foramina are challenging to segment accurately for both humans and machine. Further technical development in machine learning is necessary to accurately segment the highly anatomically variable neural foramina of the human spine.
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