  The high prevalence of spinal stenosis results in a large volume of MRI
imaging, yet interpretation can be time-consuming with high inter-reader
variability even among the most specialized radiologists. In this paper, we
develop an efficient methodology to leverage the subject-matter-expertise
stored in large-scale archival reporting and image data for a deep-learning
approach to fully-automated lumbar spinal stenosis grading. Specifically, we
introduce three major contributions: (1) a natural-language-processing scheme
to extract level-by-level ground-truth labels from free-text radiology reports
for the various types and grades of spinal stenosis (2) accurate vertebral
segmentation and disc-level localization using a U-Net architecture combined
with a spine-curve fitting method, and (3) a multi-input, multi-task, and
multi-class convolutional neural network to perform central canal and foraminal
stenosis grading on both axial and sagittal imaging series inputs with the
extracted report-derived labels applied to corresponding imaging level
segments. This study uses a large dataset of 22796 disc-levels extracted from
4075 patients. We achieve state-of-the-art performance on lumbar spinal
stenosis classification and expect the technique will increase both radiology
workflow efficiency and the perceived value of radiology reports for referring
clinicians and patients.
