Automated scoring of aortic calcification in vertebral fracture assessment images

Luke Chaplin, Tim Cootes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
PublisherSPIE
ISBN (Electronic)9781510625471
DOIs
Publication statusPublished - 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: 17 Feb 201920 Feb 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10950
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego
Period17/02/1920/02/19

Keywords

  • Abdominal Aortic Calcification
  • Cardiovascular Disease
  • Convolutional Neural Network
  • Image Analysis
  • Segmentation

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