Dr. Yan Yang Profile

Dr. Yan Yang

Senior Scientist

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Proceedings Article | 15 March 2019 Paper

Automatic two-chamber segmentation in cardiac CTA using 3D fully convolutional neural networks

Yan Yang, Osama Masoud

Proceedings Volume 10949, 109491X (2019) https://doi.org/10.1117/12.2507461

KEYWORDS: Image segmentation, 3D image processing, Convolutional neural networks, Network architectures, Heart, Convolution, Functional analysis, Computed tomography, Blood

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Cardiac chamber segmentation has proved to be essential in many clinical applications including cardiac functional analysis, myocardium analysis and electrophysiology studies for ablation planning. Traditional rule-based or modelbased approaches have been widely developed and employed, however these methods can be time consuming to run and sometimes fail when certain rules are not met. Recent advances in deep learning provide a new approach in solving these segmentation problems. In this work we employ a TensorFlow implementation of the 3D U-Net trained with 413 cardiac CTA volumes to segment the left ventricle (LV) and the left atrium (LA). The network is tested on 162 unseen volumes. For LV the Dice similarity coefficient (DSC) reaches 90.2±2.6% and for LA 87.6±7.5%. The number of training and testing samples far exceeds the common use of datasets seen in literature thanks to the existing rule-based algorithm in Vitrea®’s Cardiac Functional CT protocol which was used to provide the segmentation labels. The labels are manually filtered, and only accurate labels are kept for training and testing. For the datasets with inaccurate labels, the trained network has proved to perform better in generating more accurate boundaries around the aortic valve, mitral valve and the apex of LV. The TensorFlow implementation allows for faster training which takes 3-4 hours and inferencing which takes less than 6 seconds to simultaneously segment 12 CT volumes. This significantly reduces the pre-processing time required for cardiac functional CT studies which usually consist of 10-20 cardiac phases and take minutes to segment with traditional methods.

Proceedings Article | 28 February 2009 Paper

Quantification of stenosis in coronary artery via CTA using fuzzy distance transform

Yan Xu, Punam Saha, Guangshu Hu, Guoyuan Liang, Yan Yang, Jinzhao Geng

Proceedings Volume 7262, 72620K (2009) https://doi.org/10.1117/12.811361

KEYWORDS: Arteries, Image segmentation, Fuzzy logic, Binary data, Computer simulations, Algorithm development, Heart, Angiography, Detection and tracking algorithms, 3D image processing

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tomographic angiography (CTA) being noninvasive, economical and informative, has become a common modality for monitoring disease status and treatment effects. Here, we present a new method for detecting and quantifying coronary arterial stenosis via CTA using fuzzy distance transform (FDT) approach. FDT computes local depth at each image point in the presence of partial voluming. Coronary arterial stenoses are detected and their severities are quantified by analyzing FDT values along the medial axis of an artery obtained by skeletonization. Also, we have developed a new skeletal pruning algorithm toward improving quality of medial axes and therefore, enhancing the accuracy of stenosis detection and quantification. The method is completed using the following steps - (1) fuzzy segmentation of coronary artery via CTA, (2) FDT computation of coronary arteries, (3) medial axis computation, (4) estimation of local diameter along arteries and (5) stenosis detection and quantification of arterial blockage. Performance of the method has been quantitatively evaluated on a realistic coronary artery phantom dataset with randomly simulated stenoses and the results are compared with a classical binary algorithm. The method has also been applied on a clinical CTA dataset from thirteen patients with 59 stenoses and the results are compared with an expert's quantitative assessment of stenoses. Results of the phantom experiment indicate that the new method is significantly more accurate as compared to the conventional binary method. Also, the results of the clinical study indicate that the computerized method is highly in agreement with the expert's assessments.

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