Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data


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.

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The Titan X GPU used for this research was donated by the NVIDIA Corporation.

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Correspondence to Daniel Forsberg.

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The study was ruled exempt by the local institutional review board and informed consent was waived.


D. Forsberg is supported by a grant (2014–01422) from the Swedish Innovation Agency (VINNOVA).

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Forsberg, D., Sjöblom, E. & Sunshine, J.L. Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data. J Digit Imaging 30, 406–412 (2017).

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  • Archive
  • Artificial neural networks (ANNs)
  • Machine learning
  • Magnetic resonance imaging