Abstract
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical dataset of whole spine scans and matches or exceeds the performance of previous systems of detecting and labelling vertebrae in lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
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Acknowledgements
The authors would like to thank Dr. Sarim Ather for useful discussions on spinal anatomy and clinical approaches to diagnosing disease, as well as assistance labelling the data. Rhydian Windsor is supported by Cancer Research UK as part of the EPSRC CDT in Autonomous Intelligent Machines and Systems (EP/L015897/1). Amir Jamaludin is supported by EPSRC Programme Grant Seebibyte (EP/M013774/1). The Genodisc data was obtained during the EC FP7 project GENODISC (HEALTH-F2-2008-201626).
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Windsor, R., Jamaludin, A., Kadir, T., Zisserman, A. (2020). A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_69
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