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

TL;DR

DeepSPINE presents an automated deep learning system that combines NLP and imaging analysis to accurately segment lumbar vertebrae, localize disc levels, and grade spinal stenosis, significantly improving efficiency and consistency in MRI interpretation.

Contribution

The paper introduces a novel integrated approach using NLP for report extraction and deep learning for segmentation and grading, advancing automated spinal stenosis assessment.

Findings

Achieved state-of-the-art classification accuracy

Successfully extracted report labels for large datasets

Enhanced radiology workflow efficiency

Abstract

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.