Automated scoring of aortic calcification in vertebral fracture assessment images

Luke Chaplin; Tim Cootes

doi:10.1117/12.2512879

13 March 2019 Automated scoring of aortic calcification in vertebral fracture assessment images

Luke Chaplin, Tim Cootes

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095038 (2019) https://doi.org/10.1117/12.2512879
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Luke Chaplin and Tim Cootes "Automated scoring of aortic calcification in vertebral fracture assessment images", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095038 (13 March 2019); https://doi.org/10.1117/12.2512879

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