Ms. Yuan Zhao Profile

Yuan Zhao

at City University of Hong Kong

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Publications (3)

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Proceedings Article | 10 March 2020 Paper

Development of a 3D carotid atlas for quantification of local volume change

Xueli Chen, Yuan Zhao, J. David Spence, Bernard Chiu

Proceedings Volume 11313, 113132R (2020) https://doi.org/10.1117/12.2549521

KEYWORDS: Image registration, Image segmentation, Arteries, 3D image processing, Ultrasonography, Therapeutics, Algorithm development

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Stroke is among the leading causes of death and disability throughout the world. Carotid atherosclerosis is a focal disease predominantly occurring at bifurcations, and for this reason, local progression/regression measurements of atherosclerosis allow for more sensitive detection of treatment effect than global measurements, such as total vessel wall volume (VWV). Vessel-wall-plus-plaque thickness (VWT) change has been developed to characterize local changes and has shown to be sensitive to treatment effect, but is unable to isolate changes in individual plaque components. In this work, we propose to quantify longitudinal voxel-by-voxel plaque-and-vessel-wall volume change (ΔVVol) and represent the ΔVVol distribution on a 3D standardized atlas. Such representation allows for quantitative comparison across patients and of the measurements obtained for the same patient at different time points. We introduced a 3D non-rigid registration framework to register the carotid ultrasound images acquired at baseline and a follow-up imaging session for each patient. A 3D volume equipped with voxel-by-voxel ΔVVol was obtained by taking the divergence of the displacement field obtained in non-rigid registration. This 3D volume was uniformly sampled in the vessel wall, and the ΔVVol distribution for each patient was represented in a 3D standardized map. The proposed 3D standardized ΔVVol map allows for the characterization of feature changes on a voxel-by-voxel basis that are masked in VWT quantification. This tool has the potential to further improve the sensitivity in treatment evaluation already attained by VWT quantification.

Proceedings Article | 10 March 2020 Paper

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

Yuan Zhao, Bernard Chiu, J. David Spence

Proceedings Volume 11313, 113133E (2020) https://doi.org/10.1117/12.2549520

KEYWORDS: Ultrasonography, 3D image processing, Neural networks, Image segmentation, Clinical trials, Arteries, Computer programming

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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.

Proceedings Article | 3 March 2017 Paper

The effects of TIS and MI on the texture features in ultrasonic fatty liver images

Yuan Zhao, Xinyao Cheng, Mingyue Ding

Proceedings Volume 10134, 101343K (2017) https://doi.org/10.1117/12.2255652

KEYWORDS: Life sciences, Liver, Diagnostics, Feature extraction, Ultrasonics, Ultrasonography, Computer aided diagnosis and therapy, Image classification

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Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient’s situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

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