Automatic Interpretation of Carotid Intima-Media Thickness Videos Using Convolutional Neural Networks

Nima Tajbakhsh, Jae Y. Shin, R. Todd Hurst, Christopher B. Kendall, Jianming Liang

    Research output: Chapter in Book/Report/Conference proceedingChapter

    10 Scopus citations


    Cardiovascular disease (CVD) is the leading cause of death in the United States, yet it is largely preventable. But a critical part of prevention is identification of at-risk persons before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasonography method, has proven to be valuable. However, each CIMT examination includes several ultrasonography videos, and interpreting each CIMT video involves 3 operations: (i) selecting 3 end-diastolic ultrasonographic frames (EUFs) in the video, (ii) localizing a region of interest (ROI) in each selected frame, and (iii) tracing the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time-consuming, hindering the widespread utilization of CIMT in clinical practice. We present a new system based on convolutional neural networks to automate the entire process of CIMT video interpretation. The suggested system achieves a mean absolute error of 23.4 μm with a standard deviation of 17.3 μm for intima-media thickness measurements. The ANOVA test also yields p-values around 0.50, suggesting that there is a lack of evidence to show a difference among the CIMT measurements made by our system and those made by 3 experts. Our results suggest that the proposed system is robust against variability in CIMT measurements made by different experts.

    Original languageEnglish (US)
    Title of host publicationDeep Learning for Medical Image Analysis
    PublisherElsevier Inc.
    Number of pages27
    ISBN (Electronic)9780128104095
    ISBN (Print)9780128104088
    StatePublished - Jan 30 2017


    • Cardiovascular disease risk stratification
    • Carotid intima-media thickness
    • Convolutional neural networks
    • Deep learning

    ASJC Scopus subject areas

    • General Engineering


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