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Lashgari M, Choudhury RP, Banerjee A. Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories. Front Cardiovasc Med 2024; 11:1398290. [PMID: 39036504 PMCID: PMC11257904 DOI: 10.3389/fcvm.2024.1398290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 06/06/2024] [Indexed: 07/23/2024] Open
Abstract
Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.
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Affiliation(s)
- Mojtaba Lashgari
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Robin P. Choudhury
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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Wang W, Xia Q, Yan Z, Hu Z, Chen Y, Zheng W, Wang X, Nie S, Metaxas D, Zhang S. AVDNet: Joint coronary artery and vein segmentation with topological consistency. Med Image Anal 2024; 91:102999. [PMID: 37862866 DOI: 10.1016/j.media.2023.102999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023]
Abstract
Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries and veins have similar intensities and locate closely, which may confuse segmentation algorithms, even deep learning based ones, to obtain accurate arteries. However, it is not always feasible to re-scan the patient for better image quality. In this paper, we propose an artery and vein disentanglement network (AVDNet) for robust and accurate segmentation by incorporating the coronary vein into the segmentation task. This is the first work to segment coronary artery and vein at the same time. The AVDNet consists of an image based vessel recognition network (IVRN) and a topology based vessel refinement network (TVRN). IVRN learns to segment the arteries and veins, while TVRN learns to correct the segmentation errors based on topology consistency. We also design a novel inverse distance weighted dice (IDD) loss function to recover more thin vessel branches and preserve the vascular boundaries. Extensive experiments are conducted on a multi-center dataset of 700 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method by comparing it with state-of-the-art methods and different variants. Prediction results of the AVDNet on the Automated Segmentation of Coronary Artery Challenge dataset are avaliabel at https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation for follow-up research.
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Affiliation(s)
- Wenji Wang
- SenseTime Research, Beijing, 100080, China.
| | - Qing Xia
- SenseTime Research, Beijing, 100080, China.
| | | | | | - Yinan Chen
- SenseTime Research, Beijing, 100080, China
| | - Wen Zheng
- Center for Coronary Artery Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Xiao Wang
- Center for Coronary Artery Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Shaoping Nie
- Center for Coronary Artery Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, NJ, 08854, USA
| | - Shaoting Zhang
- SenseTime Research, Beijing, 100080, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200032, China
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Tan X, Chen X, Meng Q, Shi F, Xiang D, Chen Z, Pan L, Zhu W. OCT 2Former: A retinal OCT-angiography vessel segmentation transformer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107454. [PMID: 36921468 DOI: 10.1016/j.cmpb.2023.107454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/25/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal vessel segmentation plays an important role in the automatic retinal disease screening and diagnosis. How to segment thin vessels and maintain the connectivity of vessels are the key challenges of the retinal vessel segmentation task. Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. Aiming at make full use of its characteristic of high resolution, a new end-to-end transformer based network named as OCT2Former (OCT-a Transformer) is proposed to segment retinal vessel accurately in OCTA images. METHODS The proposed OCT2Former is based on encoder-decoder structure, which mainly includes dynamic transformer encoder and lightweight decoder. Dynamic transformer encoder consists of dynamic token aggregation transformer and auxiliary convolution branch, in which the multi-head dynamic token aggregation attention based dynamic token aggregation transformer is designed to capture the global retinal vessel context information from the first layer throughout the network and the auxiliary convolution branch is proposed to compensate for the lack of inductive bias of the transformer and assist in the efficient feature extraction. A convolution based lightweight decoder is proposed to decode features efficiently and reduce the complexity of the proposed OCT2Former. RESULTS The proposed OCT2Former is validated on three publicly available datasets i.e. OCTA-SS, ROSE-1, OCTA-500 (subset OCTA-6M and OCTA-3M). The Jaccard indexes of the proposed OCT2Former on these datasets are 0.8344, 0.7855, 0.8099 and 0.8513, respectively, outperforming the best convolution based network 1.43, 1.32, 0.75 and 1.46%, respectively. CONCLUSION The experimental results have demonstrated that the proposed OCT2Former can achieve competitive performance on retinal OCTA vessel segmentation tasks.
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Affiliation(s)
- Xiao Tan
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Xinjian Chen
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China; The State Key Laboratory of Radiation Medicine and Protection, Soochow University, Jiangsu, China
| | - Qingquan Meng
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Fei Shi
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Dehui Xiang
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Zhongyue Chen
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Lingjiao Pan
- School of Electrical and Information Engineering, Jiangsu University of Technology, Jiangsu, China
| | - Weifang Zhu
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China.
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3D vessel-like structure segmentation in medical images by an edge-reinforced network. Med Image Anal 2022; 82:102581. [DOI: 10.1016/j.media.2022.102581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 05/04/2022] [Accepted: 08/11/2022] [Indexed: 11/15/2022]
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Algarni M, Al-Rezqi A, Saeed F, Alsaeedi A, Ghabban F. Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images. PeerJ Comput Sci 2022; 8:e993. [PMID: 35721418 PMCID: PMC9202622 DOI: 10.7717/peerj-cs.993] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. METHODS In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. RESULTS The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. DISCUSSION The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.
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Affiliation(s)
- Mona Algarni
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
- Computer Science and Artificial Intelligence Department, University of Prince Mugrin, Medina, Saudi Arabia
| | - Abdulkader Al-Rezqi
- College of Medicine, King Saud bin Abdulaziz University, Jeddah, Saudi Arabia
| | - Faisal Saeed
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
- School of Computing and Digital Technology, University of Birmingham, Birmingham, United Kingdom
| | - Abdullah Alsaeedi
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | - Fahad Ghabban
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
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Meng C, Xu Y, Li N, Li Y, Ren L, Xia K. Incremental robust PCA for vessel segmentation in DSA sequences. Biomed Phys Eng Express 2022; 8. [PMID: 35439744 DOI: 10.1088/2057-1976/ac682b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/19/2022] [Indexed: 11/12/2022]
Abstract
In intervention surgery, DSA images provide a new way to observe the vessels and catheters inside the patient. Extracting coronary artery from the dynamic complex background fast improves the effectiveness directly in clinical interventional surgery. This article proposes an incremental robust principal component analysis (IRPCA) method to extract contrast-filled vessels from x-ray coronary angiograms. RPCA is a matrix decomposition method that decomposes a video matrix into foreground and background, commonly used to model complex backgrounds and extract target objects. IRPCA pre-optimizes an x-ray image sequence. When a new x-ray sequence is received, IRPCA optimizes it based on the pre-optimized matrix according to the strategy of minimizing the energy function to obtain the foreground matrix of the new sequence. Besides, based on the idea that the new x-ray sequence introduces new information to the pre-optimized matrix, we propose UIRPCA to improve the performence of IRPCA. Compared with the traditional RPCA method, IRPCA and UIRPCA save much time while ensuring that other indicators remain basically unchanged. The experiment results based on real data show the superiority of the proposed method over other RPCA algorithms.
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Affiliation(s)
- Cai Meng
- Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, People's Republic of China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, People's Republic of China
| | - Yizhou Xu
- Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, People's Republic of China
| | - Ning Li
- Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, People's Republic of China
| | - Yanggang Li
- Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, People's Republic of China
| | - Longfei Ren
- Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, People's Republic of China
| | - Kun Xia
- Beijing Chaoyang hospital, Medical University of Capital Science, Beijing 100020, People's Republic of China
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Gao Z, Wang L, Soroushmehr R, Wood A, Gryak J, Nallamothu B, Najarian K. Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features. BMC Med Imaging 2022; 22:10. [PMID: 35045816 PMCID: PMC8767756 DOI: 10.1186/s12880-022-00734-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/04/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures. METHODS A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union). RESULTS With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images. CONCLUSIONS The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.
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Affiliation(s)
- Zijun Gao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.
| | - Lu Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, USA
- Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, USA
| | - Alexander Wood
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, USA
| | - Brahmajee Nallamothu
- Department of Internal Medicine, University of Michigan, Ann Arbor, USA
- Division of Cardiovascular Diseases, University of Michigan, Ann Arbor, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA
- Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, USA
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Iyer K, Najarian CP, Fattah AA, Arthurs CJ, Soroushmehr SMR, Subban V, Sankardas MA, Nadakuditi RR, Nallamothu BK, Figueroa CA. AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography. Sci Rep 2021; 11:18066. [PMID: 34508124 PMCID: PMC8433338 DOI: 10.1038/s41598-021-97355-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/11/2021] [Indexed: 11/09/2022] Open
Abstract
Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.
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Affiliation(s)
- Kritika Iyer
- University of Michigan, 500 S State St, Ann Arbor, MI, 48109, USA
| | - Cyrus P Najarian
- University of Michigan, 500 S State St, Ann Arbor, MI, 48109, USA
| | - Aya A Fattah
- University of Michigan, 500 S State St, Ann Arbor, MI, 48109, USA
| | | | | | | | | | - Raj R Nadakuditi
- University of Michigan, 500 S State St, Ann Arbor, MI, 48109, USA
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Hao D, Ding S, Qiu L, Lv Y, Fei B, Zhu Y, Qin B. Sequential vessel segmentation via deep channel attention network. Neural Netw 2020; 128:172-187. [PMID: 32447262 DOI: 10.1016/j.neunet.2020.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 04/22/2020] [Accepted: 05/04/2020] [Indexed: 02/01/2023]
Abstract
Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method's superior performance over other methods in terms of the quantitative metrics and visual validation. To facilitate the reproductive research in XCA community, we publicly release our dataset and source codes at https://github.com/Binjie-Qin/SVS-net.
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Affiliation(s)
- Dongdong Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Song Ding
- Department of Cardiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Linwei Qiu
- School of Astronautics, Beihang University, Beijing 100191, China
| | - Yisong Lv
- School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Baowei Fei
- Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Yueqi Zhu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University, 600 Yi Shan Road, Shanghai 200233, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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