Search Results for author: Weihua Zhou

Found 8 papers, 0 papers with code

Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images

no code implementations11 Oct 2021 Chen Zhao, Shi Shi, Zhuo He, Cheng Wang, Zhongqiang Zhao, Xinli Li, Yanli Zhou, Weihua Zhou

By integrating the spatial features from each cardiac frame of gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we develop a Spatial-Temporal V-Net (S-T-V-Net) for automatic extraction of RV endocardial and epicardial contours.

Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos

no code implementations6 Oct 2021 Yinghui Meng, Minghao Dong, Xumin Dai, Haipeng Tang, Chen Zhao, Jingfeng Jiang, Shun Xu, Ying Zhou, Fubao Zhu1, Zhihui Xu, Weihua Zhou

More specifically, a detection algorithm is first used to detect the key points of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points.

Optical Flow Estimation

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images

no code implementations21 Feb 2021 Fubao Zhu, Zhengyuan Gao, Chen Zhao, Hanlei Zhu, Yong Dong, Jingfeng Jiang, Neng Dai, Weihua Zhou

In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border.

A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images

no code implementations3 Feb 2021 Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou

In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.

Interactive Segmentation

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

no code implementations25 Jan 2021 Chen Zhao, Haipeng Tang, Daniel McGonigle, Zhuo He, Chaoyang Zhang, Yu-Ping Wang, Hong-Wen Deng, Robert Bober, Weihua Zhou

We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.

A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network

no code implementations20 Jun 2020 Xuan Hua, Jungang Han, Chen Zhao, Haipeng Tang, Zhuo He, Jinshan Tang, Qing-Hui Chen, Shaojie Tang, Weihua Zhou

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals.

Classification General Classification

A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

no code implementations9 Jun 2020 Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou

During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.

Semantic Segmentation

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