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arXiv · 2007.02606

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

Rhydian Windsor·Amir Jamaludin·Timor Kadir·Andrew Zisserman
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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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Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman. 2020-07-06. A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI. https://arxiv.org/abs/2007.02606

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