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Mahendiran T, Thanou D, Senouf O, Jamaa Y, Fournier S, De Bruyne B, Abbé E, Muller O, Andò E. AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation. Int J Cardiol 2024:132598. [PMID: 39341506 DOI: 10.1016/j.ijcard.2024.132598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 09/18/2024] [Accepted: 09/23/2024] [Indexed: 10/01/2024]
Abstract
BACKGROUND Quantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g. angiography-derived fractional flow reserve). We developed AngioPy, a deep-learning model for coronary segmentation that employs user-defined ground-truth points to boost performance and minimise manual correction. We compared its performance without correction with an established QCA system. METHODS Deep learning models integrating user-defined ground-truth points were developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. External validation was performed on a dataset of 580 images. Vessel dimensions from 203 images with mild/moderate stenoses segmented by AngioPy (without correction) and an established QCA system (Medis QFR®) were compared (609 diameters). RESULTS The top-performing model had an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2 % of masks exhibiting an F1 score > 0.8. Similar results were seen with external validation (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Vessel dimensions from AngioPy exhibited excellent agreement with QCA (r = 0.96 [95 % CI 0.95-0.96], p < 0.001; mean difference - 0.18 mm [limits of agreement (LOA): -0.84 to 0.49]), including the minimal luminal diameter (r = 0.93 [95 % CI 0.91-0.95], p < 0.001; mean difference - 0.06 mm [LOA: -0.70 to 0.59]). CONCLUSION AngioPy, an open-source tool, performs rapid and accurate coronary segmentation without the need for manual correction. It has the potential to increase the accuracy and efficiency of QCA.
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Affiliation(s)
- Thabo Mahendiran
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland; Mathematical Data Science, EPFL, Lausanne, Switzerland
| | - Dorina Thanou
- Mathematical Data Science, EPFL, Lausanne, Switzerland
| | - Ortal Senouf
- Mathematical Data Science, EPFL, Lausanne, Switzerland
| | | | - Stephane Fournier
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Bernard De Bruyne
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland; Department of Cardiology, OLV Cardiovascular Center, Aalst, Belgium
| | - Emmanuel Abbé
- Mathematical Data Science, EPFL, Lausanne, Switzerland
| | - Olivier Muller
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Edward Andò
- Center for Imaging, EPFL, Lausanne, Switzerland
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Xu H, Wu Y. G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation. Neural Netw 2024; 176:106356. [PMID: 38723311 DOI: 10.1016/j.neunet.2024.106356] [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: 10/11/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/17/2024]
Abstract
Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.
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Affiliation(s)
- Hao Xu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yun Wu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
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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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Yang X, Zheng Y, Mei C, Jiang G, Tian B, Wang L. UGLS: an uncertainty guided deep learning strategy for accurate image segmentation. Front Physiol 2024; 15:1362386. [PMID: 38651048 PMCID: PMC11033460 DOI: 10.3389/fphys.2024.1362386] [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: 01/05/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).
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Affiliation(s)
- Xiaoguo Yang
- Wenzhou People’s Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou, China
| | - Yanyan Zheng
- Wenzhou People’s Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou, China
| | - Chenyang Mei
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Gaoqiang Jiang
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Bihan Tian
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Lei Wang
- School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [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: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Park J, Kweon J, Kim YI, Back I, Chae J, Roh JH, Kang DY, Lee PH, Ahn JM, Kang SJ, Park DW, Lee SW, Lee CW, Park SW, Park SJ, Kim YH. Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography. Med Phys 2023; 50:7822-7839. [PMID: 37310802 DOI: 10.1002/mp.16554] [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: 07/10/2022] [Revised: 03/29/2023] [Accepted: 05/26/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. PURPOSE This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. METHODS Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. RESULTS The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. CONCLUSION Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.
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Affiliation(s)
- Jeeone Park
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jihoon Kweon
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young In Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Inwook Back
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Jihye Chae
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Jae-Hyung Roh
- Department of Cardiology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Daejeon, South Korea
| | - Do-Yoon Kang
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Pil Hyung Lee
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Jung-Min Ahn
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Soo-Jin Kang
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Duk-Woo Park
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Seung-Whan Lee
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Cheol Whan Lee
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Seong-Wook Park
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Seung-Jung Park
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Internal Medicine, Medical Center, University of Ulsan College of Medicine, Asan, Seoul, South Korea
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Zhao C, Xu Z, Hung GU, Zhou W. EAGMN: Coronary artery semantic labeling using edge attention graph matching network. Comput Biol Med 2023; 166:107469. [PMID: 37725850 PMCID: PMC11073582 DOI: 10.1016/j.compbiomed.2023.107469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/14/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023]
Abstract
Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis. However, deep-learning-based models face challenges in generating semantic segmentation for coronary arteries due to the morphological similarity among different types of arteries. To address this challenge, we propose an innovative approach called the Edge Attention Graph Matching Network (EAGMN) for coronary artery semantic labeling. Inspired by the learning process of interventional cardiologists in interpreting ICA images, our model compares arterial branches between two individual graphs generated from different ICAs. We begin with extracting individual graphs based on the vascular tree obtained from the ICA. Each node in the individual graph represents an arterial segment, and the EAGMN aims to learn the similarity between nodes from the two individual graphs. By converting the coronary artery semantic segmentation task into a graph node similarity comparison task, identifying the node-to-node correspondence would assign semantic labels for each arterial branch. More specifically, the EAGMN utilizes the association graph constructed from the two individual graphs as input. A graph attention module is employed for feature embedding and aggregation, while a decoder generates the linear assignment for node-to-node semantic mapping. Based on the learned node-to-node relationships, unlabeled coronary arterial segments are classified using the labeled coronary arterial segments, thereby achieving semantic labeling. A dataset with 263 labeled ICAs is used to train and validate the EAGMN. Experimental results indicate the EAGMN achieved a weighted accuracy of 0.8653, a weighted precision of 0.8656, a weighted recall of 0.8653 and a weighted F1-score of 0.8643. Furthermore, we employ ZORRO to provide interpretability and explainability of the graph matching for artery semantic labeling. These findings highlight the potential of the EAGMN for accurate and efficient coronary artery semantic labeling using ICAs. By leveraging the inherent characteristics of ICAs and incorporating graph matching techniques, our proposed model provides a promising solution for improving CAD diagnosis and treatment.
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Affiliation(s)
- Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, USA
| | - Zhihui Xu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guang-Uei Hung
- Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cyber-systems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA.
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Shen Y, Chen Z, Tong J, Jiang N, Ning Y. DBCU-Net: deep learning approach for segmentation of coronary angiography images. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2023; 39:1571-1579. [PMID: 37017823 DOI: 10.1007/s10554-023-02849-3] [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: 09/03/2022] [Accepted: 03/30/2023] [Indexed: 04/06/2023]
Abstract
Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.
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Affiliation(s)
- Yuqiang Shen
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua, China
| | - Zhe Chen
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Jijun Tong
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Nan Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China.
| | - Yun Ning
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
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Nobre Menezes M, Silva JL, Silva B, Rodrigues T, Guerreiro C, Guedes JP, Santos MO, Oliveira AL, Pinto FJ. Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model. Int J Cardiovasc Imaging 2023; 39:1385-1396. [PMID: 37027105 PMCID: PMC10250252 DOI: 10.1007/s10554-023-02839-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/18/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. METHODS Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. RESULTS 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. CONCLUSION the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.
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Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal.
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal.
| | - João Lourenço Silva
- INESC-ID / Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
| | - Tiago Rodrigues
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
| | | | - João Pedro Guedes
- Unidade de Hemodinâmica e Cardiologia de Intervenção, Serviço de Cardiologia, Centro Hospitalar Universitário do Algarve, Hospital de Faro, Faro, Portugal
| | - Manuel Oliveira Santos
- Unidade de Intervenção Cardiovascular, Serviço de Cardiologia do Centro Hospitalar e Universitário de Coimbra, Praceta Professor Mota Pinto, Coimbra, 3004-561, Portugal
- Faculdade de Medicina da Universidade de Coimbra, R. Larga 2, Coimbra, 3000-370, Portugal
| | - Arlindo L Oliveira
- INESC-ID / Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
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Zhang Y, Gao Y, Zhou G, He J, Xia J, Peng G, Lou X, Zhou S, Tang H, Chen Y. Centerline-supervision multi-task learning network for coronary angiography segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Zhang H, Gao Z, Zhang D, Hau WK, Zhang H. Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:864-879. [PMID: 36327189 DOI: 10.1109/tmi.2022.3219126] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.
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12
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Nobre Menezes M, Lourenço-Silva J, Silva B, Rodrigues O, Francisco ARG, Carrilho Ferreira P, Oliveira AL, Pinto FJ. Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance. Rev Port Cardiol 2022; 41:1011-1021. [PMID: 36511271 DOI: 10.1016/j.repc.2022.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/03/2022] [Indexed: 01/11/2023] Open
Abstract
INTRODUCTION AND OBJECTIVES Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians. METHODS Patients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0-100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment. RESULTS 1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks. CONCLUSIONS We successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.
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Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal.
| | | | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Oliveira Rodrigues
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Rita G Francisco
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Pedro Carrilho Ferreira
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | | | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
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Dense Convolutional Network and Its Application in Medical Image Analysis. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2384830. [PMID: 35509707 PMCID: PMC9060995 DOI: 10.1155/2022/2384830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/23/2022] [Indexed: 12/28/2022]
Abstract
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.
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Molenaar MA, Selder JL, Nicolas J, Claessen BE, Mehran R, Bescós JO, Schuuring MJ, Bouma BJ, Verouden NJ, Chamuleau SAJ. Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease. Curr Cardiol Rep 2022; 24:365-376. [PMID: 35347566 PMCID: PMC8979928 DOI: 10.1007/s11886-022-01655-y] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/03/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). RECENT FINDINGS Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31-14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.
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Affiliation(s)
- Mitchel A Molenaar
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands.
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands.
| | - Jasper L Selder
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Johny Nicolas
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1030, New York, NY, 10029-6574, USA
| | - Bimmer E Claessen
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Roxana Mehran
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1030, New York, NY, 10029-6574, USA
| | | | - Mark J Schuuring
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Berto J Bouma
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Niels J Verouden
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Steven A J Chamuleau
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers-Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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15
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Qi Y, Xu H, He Y, Li G, Li Z, Kong Y, Coatrieux JL, Shu H, Yang G, Tu S. Examinee-Examiner Network: Weakly Supervised Accurate Coronary Lumen Segmentation Using Centerline Constraint. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:9429-9441. [PMID: 34757906 DOI: 10.1109/tip.2021.3125490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Accurate coronary lumen segmentation on coronary-computed tomography angiography (CCTA) images is crucial for quantification of coronary stenosis and the subsequent computation of fractional flow reserve. Many factors including difficulty in labeling coronary lumens, various morphologies in stenotic lesions, thin structures and small volume ratio with respect to the imaging field complicate the task. In this work, we fused the continuity topological information of centerlines which are easily accessible, and proposed a novel weakly supervised model, Examinee-Examiner Network (EE-Net), to overcome the challenges in automatic coronary lumen segmentation. First, the EE-Net was proposed to address the fracture in segmentation caused by stenoses by combining the semantic features of lumens and the geometric constraints of continuous topology obtained from the centerlines. Then, a Centerline Gaussian Mask Module was proposed to deal with the insensitiveness of the network to the centerlines. Subsequently, a weakly supervised learning strategy, Examinee-Examiner Learning, was proposed to handle the weakly supervised situation with few lumen labels by using our EE-Net to guide and constrain the segmentation with customized prior conditions. Finally, a general network layer, Drop Output Layer, was proposed to adapt to the class imbalance by dropping well-segmented regions and weights the classes dynamically. Extensive experiments on two different data sets demonstrated that our EE-Net has good continuity and generalization ability on coronary lumen segmentation task compared with several widely used CNNs such as 3D-UNet. The results revealed our EE-Net with great potential for achieving accurate coronary lumen segmentation in patients with coronary artery disease. Code at http://github.com/qiyaolei/Examinee-Examiner-Network.
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Yoo J, Jun TJ, Kim YH. xECGNet: Fine-tuning attention map within convolutional neural network to improve detection and explainability of concurrent cardiac arrhythmias. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106281. [PMID: 34333207 DOI: 10.1016/j.cmpb.2021.106281] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
Background and objectiveDetecting abnormal patterns within an electrocardiogram (ECG) is crucial for diagnosing cardiovascular diseases. We start from two unresolved problems in applying deep-learning-based ECG classification models to clinical practice: first, although multiple cardiac arrhythmia (CA) types may co-occur in real life, the majority of previous detection methods have focused on one-to-one relationships between ECG and CA type, and second, it has been difficult to explain how neural-network-based CA classifiers make decisions. We hypothesize that fine-tuning attention maps with regard to all possible combinations of ground-truth (GT) labels will improve both the detection and interpretability of co-occurring CAs. Methods To test our hypothesis, we propose an end-to-end convolutional neural network (CNN), xECGNet, that fine-tunes the attention map to resemble the averaged response maps of GT labels. Fine-tuning is achieved by adding to the objective function a regularization loss between the attention map and the reference (averaged) map. Performance is assessed by F1 score and subset accuracy. Results The main experiment demonstrates that fine-tuning alone significantly improves a model's multilabel subset accuracy from 75.8% to 84.5% when compared with the baseline model. Also, xECGNet shows the highest F1 score of 0.812 and yields a more explainable map that encompasses multiple CA types, when compared to other baseline methods. Conclusions xECGNet has implications in that it tackles the two obstacles for the clinical application of CNN-based CA detection models with a simple solution of adding one additional term to the objective function.
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Affiliation(s)
- Jungsun Yoo
- Division of Cardiology, Asan Medical Center, Seoul, Republic of Korea
| | - Tae Joon Jun
- Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
| | - Young-Hak Kim
- Division of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
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17
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Du T, Xie L, Zhang H, Liu X, Wang X, Chen D, Xu Y, Sun Z, Zhou W, Song L, Guan C, Lansky AJ, Xu B. Training and validation of a deep learning architecture for the automatic analysis of coronary angiography. EUROINTERVENTION 2021; 17:32-40. [PMID: 32830647 PMCID: PMC9753915 DOI: 10.4244/eij-d-20-00570] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND In recent years, the use of deep learning has become more commonplace in the biomedical field and its development will greatly assist clinical and imaging data interpretation. Most existing machine learning methods for coronary angiography analysis are limited to a single aspect. AIMS We aimed to achieve an automatic and multimodal analysis to recognise and quantify coronary angiography, integrating multiple aspects, including the identification of coronary artery segments and the recognition of lesion morphology. METHODS A data set of 20,612 angiograms was retrospectively collected, among which 13,373 angiograms were labelled with coronary artery segments, and 7,239 were labelled with special lesion morphology. Trained and optimised by these labelled data, one network recognised 20 different segments of coronary arteries, while the other detected lesion morphology, including measures of lesion diameter stenosis as well as calcification, thrombosis, total occlusion, and dissection detections in an input angiogram. RESULTS For segment prediction, the recognition accuracy was 98.4%, and the recognition sensitivity was 85.2%. For detecting lesion morphologies including stenotic lesion, total occlusion, calcification, thrombosis, and dissection, the F1 scores were 0.829, 0.810, 0.802, 0.823, and 0.854, respectively. Only two seconds were needed for the automatic recognition. CONCLUSIONS Our deep learning architecture automatically provides a coronary diagnostic map by integrating multiple aspects. This helps cardiologists to flag and diagnose lesion severity and morphology during the intervention.
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Affiliation(s)
- Tianming Du
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Lihua Xie
- Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Honggang Zhang
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Xuqing Liu
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaofei Wang
- Beijing Redcdn Technology Co., Ltd, Beijing, China
| | - Donghao Chen
- Beijing Redcdn Technology Co., Ltd, Beijing, China
| | - Yang Xu
- Beijing Redcdn Technology Co., Ltd, Beijing, China
| | - Zhongwei Sun
- Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Wenhui Zhou
- Beijing Redcdn Technology Co., Ltd, Beijing, China
| | - Lei Song
- Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Changdong Guan
- Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | | | - Bo Xu
- Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, A 167, Beilishi Road, Xicheng District, Beijing, 100037, China
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Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5548517. [PMID: 33898624 PMCID: PMC8052146 DOI: 10.1155/2021/5548517] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/04/2021] [Accepted: 03/23/2021] [Indexed: 11/24/2022]
Abstract
Each level of the coronary artery has different sizes and properties. The primary coronary arteries usually have high contrast to the background, while the secondary coronary arteries have low contrast to the background and thin structures. Furthermore, several small vessels are disconnected or broken up vascular segments. It is a challenging task to use a single model to segment all coronary artery sizes. To overcome this problem, we propose a novel segmenting method for coronary artery extraction from angiograms based on the primary and secondary coronary artery. Our method is a coarse-to-fine strategic approach for extracting coronary arteries in many different sizes. We construct the first U-net model to segment the main coronary artery extraction and build a new algorithm to determine the junctions of the main coronary artery with the secondary coronary artery. Using these junctions, we determine regions of the secondary coronary arteries (rectangular regions) for a secondary coronary artery-extracted segment with the second U-net model. The experiment result is 76.40% in terms of Dice coefficient on coronary X-ray datasets. The proposed approach presents its potential in coronary vessel segmentation.
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Ahn I, Na W, Kwon O, Yang DH, Park GM, Gwon H, Kang HJ, Jeong YU, Yoo J, Kim Y, Jun TJ, Kim YH. CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases. BMC Med Inform Decis Mak 2021; 21:29. [PMID: 33509180 PMCID: PMC7842077 DOI: 10.1186/s12911-021-01392-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/10/2021] [Indexed: 01/23/2023] Open
Abstract
Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.
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Affiliation(s)
- Imjin Ahn
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Wonjun Na
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Osung Kwon
- Division of Cardiology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong Hyun Yang
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyung-Min Park
- Department of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - Hansle Gwon
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Jun Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olumpicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Yeon Uk Jeong
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungsun Yoo
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olumpicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Yunha Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Tae Joon Jun
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, 88, Olumpicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
| | - Young-Hak Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olumpicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
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20
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Yuan AY, Gao Y, Peng L, Zhou L, Liu J, Zhu S, Song W. Hybrid deep learning network for vascular segmentation in photoacoustic imaging. BIOMEDICAL OPTICS EXPRESS 2020; 11:6445-6457. [PMID: 33282500 PMCID: PMC7687958 DOI: 10.1364/boe.409246] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/02/2020] [Accepted: 10/06/2020] [Indexed: 05/04/2023]
Abstract
Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.
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Affiliation(s)
- Alan Yilun Yuan
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
- These authors contributed equally to this work
| | - Yang Gao
- Nanophotonics Research Center, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
- These authors contributed equally to this work
| | - Liangliang Peng
- Nanophotonics Research Center, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| | - Lingxiao Zhou
- Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
- Department of Respiratory Medicine, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Jun Liu
- Tianjin Union Medical Centre, Tianjin, China
| | - Siwei Zhu
- Tianjin Union Medical Centre, Tianjin, China
| | - Wei Song
- Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
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Yang S, Kweon J, Roh JH, Lee JH, Kang H, Park LJ, Kim DJ, Yang H, Hur J, Kang DY, Lee PH, Ahn JM, Kang SJ, Park DW, Lee SW, Kim YH, Lee CW, Park SW, Park SJ. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep 2019; 9:16897. [PMID: 31729445 PMCID: PMC6858336 DOI: 10.1038/s41598-019-53254-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 10/25/2019] [Indexed: 11/17/2022] Open
Abstract
X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.
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Affiliation(s)
- Su Yang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jihoon Kweon
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Korea.
| | - Jae-Hyung Roh
- Department of Cardiology in Internal Medicine, School of Medicine, Chungnam National University, Chungnam National University Hospital, Daejeon, Korea
| | - Jae-Hwan Lee
- Department of Cardiology in Internal Medicine, School of Medicine, Chungnam National University, Chungnam National University Hospital, Daejeon, Korea
| | - Heejun Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Lae-Jeong Park
- Department of Electronic Engineering, Gangneung-Wonju National University, Gangneung, Korea
| | - Dong Jun Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyeonkyeong Yang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jaehee Hur
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Do-Yoon Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Pil Hyung Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jung-Min Ahn
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Soo-Jin Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Duk-Woo Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Whan Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Cheol Whan Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seong-Wook Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Jung Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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