2017/01/01 by Gabriele Abbati, Stefan Bauer, Abbati, Gabriele +13
Engineering · Medicine · #Applications (stat.AP) #FOS: Computer and information sciences #Medical Imaging and Analysis #Spinal Fractures and Fixation Techniques #Spine and Intervertebral Disc Pathology
paper · doi:10.48550/arxiv.1703.07137
openalex publication_date 2017/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis(LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon shows large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans.