Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

40 Citations (Scopus)

Abstract

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Subtitle of host publication18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I
PublisherSpringer
Pages589-596
Number of pages8
ISBN (Electronic)978-3-319-24553-9
ISBN (Print)978-3-319-24552-2
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 5 Oct 20159 Oct 2015
Conference number: 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9349
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Abbreviated titleMICCAI 2015
CountryGermany
CityMunich
Period5/10/159/10/15

Keywords

  • Automatic coronary artery calcium scoring
  • Cardiac CTA
  • Convolutional neural network
  • Random Forest

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