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The high prevalence of spinal stenosis results in a large volume of MRIimaging, yet interpretation can be time-consuming with high inter-readervariability even among the most specialized radiologists. In this paper, wedevelop an efficient methodology to leverage the subject-matter-expertisestored in large-scale archival reporting and image data for a deep-learningapproach to fully-automated lumbar spinal stenosis grading. Specifically, weintroduce three major contributions: (1) a natural-language-processing schemeto extract level-by-level ground-truth labels from free-text radiology reportsfor the various types and grades of spinal stenosis (2) accurate vertebralsegmentation and disc-level localization using a U-Net architecture combinedwith a spine-curve fitting method, and (3) a multi-input, multi-task, andmulti-class convolutional neural network to perform central canal and foraminalstenosis grading on both axial and sagittal imaging series inputs with theextracted report-derived labels applied to corresponding imaging levelsegments. This study uses a large dataset of 22796 disc-levels extracted from4075 patients. We achieve state-of-the-art performance on lumbar spinalstenosis classification and expect the technique will increase both radiologyworkflow efficiency and the perceived value of radiology reports for referringclinicians and patients.

DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning