title: Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data.
abstract: The purpose of this study was to investigate the potential of using clinically provided spine label annotations stored in a single institution image archive as training data for deep learning-based vertebral detection and labeling pipelines. Lumbar and cervical magnetic resonance imaging cases with annotated spine labels were identified and exported from an image archive. Two separate pipelines were configured and trained for lumbar and cervical cases respectively, using the same setup with convolutional neural networks for detection and parts-based graphical models to label the vertebrae. The detection sensitivity, precision and accuracy rates ranged between 99.1-99.8, 99.6-100, and 98.8-99.8% respectively, the average localization error ranges were 1.18-1.24 and 2.38-2.60 mm for cervical and lumbar cases respectively, and with a labeling accuracy of 96.0-97.0%. Failed labeling results typically involved failed S1 detections or missed vertebrae that were not fully visible on the image. These results show that clinically annotated image data from one image archive is sufficient to train a deep learning-based pipeline for accurate detection and labeling of MR images depicting the spine. Further, these results support using deep learning to assist radiologists in their work by providing highly accurate labels that only require rapid confirmation.
authors.affiliation: Sectra, Teknikringen 20, 583 30, Linköping, SE, Sweden. daniel.forsberg@sectra.com.|Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. daniel.forsberg@sectra.com.
authors.forename: Daniel
authors.identifier: http://orcid.org/0000-0003-0908-9470
authors.initials: D
authors.lastname: Forsberg
authors.affiliation: Sectra, Teknikringen 20, 583 30, Linköping, SE, Sweden.
authors.forename: Erik
authors.initials: E
authors.lastname: Sjöblom
authors.affiliation: Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Sunshine
mesh_terms: D002574:Cervical Vertebrae / Q000000981:diagnostic imaging; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D011873:Radiology Information Systems; D012680:Sensitivity and Specificity; D013131:Spine / Q000000981:diagnostic imaging*; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging
keywords: Archive; Artificial neural networks (ANNs); Machine learning; Magnetic resonance imaging