A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI

• URL: http://arxiv.org/abs/2007.02606v3
• Date: Mon, 13 Jul 2020 13:10:27 GMT
• Title: A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
• Authors: Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
• Abstract summary: 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. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
• Score: 70.04389979779195
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 on 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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