T1, T2, STIR, TIRM and FLAIR) fields of view (e.g. Furthermore, for clinical use, labelling must be robust to:variations in spinal anatomy (such as collapsed vertebrae, hemivertebrae andfused vertebra) vertebrae numbers – around 11.3% of the population have onemore or one less mobile vertebra ) different imaging parameters includingMR weighting (e.g. Labelling by simply counting down from the C2vertebra is problematic as it assumes that all vertebrae have been detected,C2 is visible, and that every patient has the same number of vertebrae whichis not always true. One of the mostobvious is that vertebrae are highly repetitive and hence distinguishing betweendifferent levels can be hard.
VISUAL WEIGHTING INMR REGISTRATION
Finally, vertebrae can actas points to allow registration between different scans.There are several issues that make this task challenging. Secondly, vertebral bodies can be used to infer other spinalstructures of interest such as the spinal cord or ribs. Firstly, auto-mated diagnosis of many spinal diseases such as disc degeneration or spinalstenosis relies on accurate localisation of vertebral structures or, in the caseof pathological scoliosis, lordosis and kyphosis, analysing the geometry ofthe spinal column. This is an important task for several reasons. The objective of this paper is automated vertebrae detection and identificationof vertebral levels. Finally, we demonstrate the clinical ap-plicability of this method, using it for automated scoliosis detection inboth lumbar and whole spine MR scans. Theresulting system achieves 98.1% detection rate and 96.5% identificationrate on a challenging clinical dataset of whole spine scans and matches orexceeds the performance of previous systems of detecting and labellingvertebrae in lumbar-only scans. The method can be applied without modification to lumbar, cervicaland thoracic-only scans across a range of different MR sequences. This involves usinga learnt vector field to group detected vertebrae corners together into in-dividual vertebral bodies and convolutional image-to-image translationfollowed by beam search to label vertebral levels in a self-consistent man-ner. We propose a novel convolutional method for the detectionand identification of vertebrae in whole spine MRIs. Rhydian Windsor, Amir Jamaludin, Timor Kadir, and Andrew Zisserman Visual Geometry Group, Department of Engineering Science, University of Oxford Plexalis Ltd AA Convolutional Approach to VertebraeDetection and Labelling in Whole Spine MRI
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