{"id":32915,"date":"2026-01-21T17:02:54","date_gmt":"2026-01-21T17:02:54","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/segmentation-and-analysis-of-lumbar-spine-mri-scans-for-vertebral-body-measurements\/"},"modified":"2026-09-17T11:26:52","modified_gmt":"2026-09-17T11:26:52","slug":"segmentation-and-analysis-of-lumbar-spine-mri-scans-for-vertebral-body-measurements","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/segmentation-and-analysis-of-lumbar-spine-mri-scans-for-vertebral-body-measurements\/","title":{"rendered":"Segmentation and analysis of lumbar spine mri scans for vertebral body measurements"},"content":{"rendered":"<p>This 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This 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 [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32915","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32915","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32915\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32915"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}