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.
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.
Lower back pain and pathologies related to it are one of the most common results for a referral to a neurosurgical clinic in the developed and the developing world. Quantitative evaluation of these pathologies is a challenge. Image based measurements of angles/vertebral heights and disks could provide a potential quantitative biomarker for tracking and measuring these pathologies. Detection of vertebral bodies is a key element and is the focus of the current work. From the variety of medical imaging techniques, MRI and CT scans have been typically used for developing image segmentation methods. However, CT scans are known to give a large dose of x-rays, increasing cancer risk [8]. MRI can be substituted for CTs when the risk is high [8] but are difficult to obtain in smaller facilities due to cost and lack of expertise in the field [2]. X-rays provide another option with its ability to control the x-ray dosage, especially for young people, and its accessibility for smaller facilities. Hence, the ability to create quantitative biomarkers from x-ray data is especially valuable. Here, we develop a multiscale template matching, inspired by [9], to detect centers of vertebral bodies from x-ray data. The immediate application of such detection lies in developing quantitative biomarkers and in querying similar images in a database. Previously, shape similarity classification methods have been used to address this problem, but these are challenging to use in the presence of variation due to gross pathology and even subtle effects [1].
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