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Automated scoring of aortic calcification in vertebral fracture assessment images
Author(s): Luke Chaplin; Tim Cootes
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Paper Abstract

The severity of abdominal aortic calcification (AAC) is a strong, independent predictor of cardiovascular disease (CVD). Vertebral fracture assessment (VFA) is a low radiation screening tool which can be used to incidentally measure AAC. This work compares the performance of Haar feature random forest classification with a Unet based convolutional neural network (CNN) segmentation, to automatically quantify AAC. Clinical semiquantitative scores were also generated using U-net. Scores were calculated using the relative length of labelled calcification and compared to manual scoring. The U-net outperformed the random forest, showed sensible segmentations and AAC scores, though it could not match human annotation accuracy.

Paper Details

Date Published: 13 March 2019
PDF: 9 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095038 (13 March 2019); doi: 10.1117/12.2512879
Show Author Affiliations
Luke Chaplin, The Univ. of Manchester (United Kingdom)
Tim Cootes, The Univ. of Manchester (United Kingdom)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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