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Proceedings Paper

Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis
Author(s): Yuan Zhao; J. David Spence; Bernard Chiu
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Paper 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 ∆VWTpatch, 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 (∆VWTpoint) in 40 subjects involved in a placebo-controlled clinical trial of the efficacy of pomegranate. ∆VWTpatch detected a significant difference between the pomegranate and placebo groups (p = 0.017), but not ∆VWTpoint (p = 0.056). The sample size required by ∆VWTpatch to establish significance was 37% smaller than that by ∆VWTpoint.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133E (10 March 2020); doi: 10.1117/12.2549520
Show Author Affiliations
Yuan Zhao, City Univ. of Hong Kong (Hong Kong, China)
J. David Spence, Robarts Research Institute (Canada)
Bernard Chiu, City Univ. of Hong Kong (Hong Kong, China)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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