Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis

Yuan Zhao; J. David Spence; Bernard Chiu

doi:10.1117/12.2549520

10 March 2020 Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis

Yuan Zhao, J. David Spence, Bernard Chiu

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113133E (2020) https://doi.org/10.1117/12.2549520
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Sensitive and cost-effective biomarkers for carotid atherosclerosis are required to evaluate the efficacy of dietary and medical treatments. Carotid atherosclerosis is a focal disease predominantly occurring in bends and bifurcations. For this reason, we have previously developed a method to measure local vessel-wall-plus-plaque thickness (VWT); a biomarker based on a weighted average of the point-wise ΔVWT was also developed and validated to be sensitive to treatment effect. However, the weight determined on a point-by-point basis did not take into account the spatial correlation of ΔVWT in neighboring points. In this paper, we developed a biomarker that is able to characterize the correlation within each local patch of the VWT map. The deep autoencoder (DAE) initialized by the stacked restricted Boltzmann machines (RBMs) was introduced to learn a compact feature representation of each patch in the 2D VWT map. The patch-based feature change was obtained by taking the difference between the features obtained at baseline and a follow-up imaging session. The new biomarker, denoted by ∆VWT_patch, was computed by taking a weighted average of the patch-based feature change. The sensitivity of the patch-based average was compared with that of the point-wise average (∆VWT_point) in 40 subjects involved in a placebo-controlled clinical trial of the efficacy of pomegranate. ∆VWT_patch detected a significant difference between the pomegranate and placebo groups (p = 0.017), but not ∆VWT_point (p = 0.056). The sample size required by ∆VWT_patch to establish significance was 37% smaller than that by ∆VWT_point.

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Yuan Zhao, J. David Spence, and Bernard Chiu "Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133E (10 March 2020); https://doi.org/10.1117/12.2549520

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