2022/10/26 by Narasimharao Kowlagi, Kowlagi, Narasimharao, Terence McSweeney +10 · 1 citation
Engineering · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Imaging and Analysis #Musculoskeletal pain and rehabilitation #Spine and Intervertebral Disc Pathology #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2210.14597
openalex publication_date 2022/10/26 · openalex created_date 2022/11/02 · openalex updated_date 2026/07/28
This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attributed to the use of transformer architectures. In this work, we argue that with a well-tuned three-stage pipeline comprising semantic segmentation, localization, and classification, convolutional networks outperform the state-of-the-art approaches. We conducted an ablation study of the existing methods in a population cohort, and report performance generalization across various subgroups. Our code is publicly available to advance research on disc degeneration and low back pain.