Schneider, HelenHelenSchneiderBiesner, DavidDavidBiesnerAshokan, AkashAkashAshokanBroß, MaximilianMaximilianBroßKador, RebeccaRebeccaKadorBagyo, GaborGaborBagyoDankerl, PeterPeterDankerlRagab, HaissamHaissamRagabYamamura, JinJinYamamuraHalscheidt, SandraSandraHalscheidtLabisch, ChristophChristophLabischSifa, RafetRafetSifa2024-02-022024-02-022023https://publica.fraunhofer.de/handle/publica/45961610.14428/esann/2023.ES2023-88This paper investigates a data- and knowledge-driven approach to automatically analyze lumbar MRI scans. The dataset used is an in-house dataset of 142 sagital lumbar spine images from German radiology practices of the evidia GmbH. We implement state-of-the-art deep learning methods to segment the individual vertebral bodies. Overall, a very accurate segmentation performance of 97% Dice Score was achieved. Based on this segmentation, pathologically relevant distances are calculated using rule-based computer vision methods. We focus on the anterior, posterior and middle height of a vertebra and the anterior and posterior distances between two lumbar vertebrae. We demonstrate the clinical value of this approach through a quantitative and qualitative result analysis.enconference paper