1
|
Avram R, Olgin JE, Ahmed Z, Verreault-Julien L, Wan A, Barrios J, Abreau S, Wan D, Gonzalez JE, Tardif JC, So DY, Soni K, Tison GH. CathAI: fully automated coronary angiography interpretation and stenosis estimation. NPJ Digit Med 2023; 6:142. [PMID: 37568050 PMCID: PMC10421915 DOI: 10.1038/s41746-023-00880-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.
Collapse
Affiliation(s)
- Robert Avram
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
- Division of Cardiology, Department of Medicine, Montreal Heart Institute - Université de Montréal, 5000 Rue Belanger, Montreal, QC, H1T 1C8, Canada
| | - Jeffrey E Olgin
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA
| | - Zeeshan Ahmed
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Louis Verreault-Julien
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Alvin Wan
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA
| | - Joshua Barrios
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Sean Abreau
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Derek Wan
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA
| | - Joseph E Gonzalez
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA
| | - Jean-Claude Tardif
- Division of Cardiology, Department of Medicine, Montreal Heart Institute - Université de Montréal, 5000 Rue Belanger, Montreal, QC, H1T 1C8, Canada
| | - Derek Y So
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Krishan Soni
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Geoffrey H Tison
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA.
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, 94158, USA.
| |
Collapse
|
2
|
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.
Collapse
|
3
|
Cong C, Kato Y, Vasconcellos HDD, Ostovaneh MR, Lima JAC, Ambale-Venkatesh B. Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography. Front Cardiovasc Med 2023; 10:944135. [PMID: 36824452 PMCID: PMC9941145 DOI: 10.3389/fcvm.2023.944135] [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: 05/14/2022] [Accepted: 01/16/2023] [Indexed: 02/09/2023] Open
Abstract
Background Automatic coronary angiography (CAG) assessment may help in faster screening and diagnosis of stenosis in patients with atherosclerotic disease. We aimed to provide an end-to-end workflow that separates cases with normal or mild stenoses from those with higher stenosis severities to facilitate safety screening of a large volume of the CAG images. Methods A deep learning-based end-to-end workflow was employed as follows: (1) Candidate frame selection from CAG videograms with Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) network, (2) Stenosis classification with Inception-v3 using 2 or 3 categories (<25%, >25%, and/or total occlusion) with and without redundancy training, and (3) Stenosis localization with two methods of class activation map (CAM) and anchor-based feature pyramid network (FPN). Overall 13,744 frames from 230 studies were used for the stenosis classification training and fourfold cross-validation for image-, artery-, and per-patient-level. For the stenosis localization training and fourfold cross-validation, 690 images with > 25% stenosis were used. Results Our model achieved an accuracy of 0.85, sensitivity of 0.96, and AUC of 0.86 in per-patient level stenosis classification. Redundancy training was effective to improve classification performance. Stenosis position localization was adequate with better quantitative results in anchor-based FPN model, achieving global-sensitivity for left coronary artery (LCA) and right coronary artery (RCA) of 0.68 and 0.70. Conclusion We demonstrated a fully automatic end-to-end deep learning-based workflow that eliminates the vessel extraction and segmentation step in coronary artery stenosis classification and localization on CAG images. This tool may be useful to facilitate safety screening in high-volume centers and in clinical trial settings.
Collapse
Affiliation(s)
- Chao Cong
- Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
- School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China
| | - Yoko Kato
- Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | | | | | - Joao A. C. Lima
- Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | | |
Collapse
|
4
|
Meng Y, Du Z, Zhao C, Dong M, Pienta D, Tang J, Zhou W. Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Technol Health Care 2023; 31:2303-2317. [PMID: 37545276 DOI: 10.3233/thc-230278] [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] [Indexed: 08/08/2023]
Abstract
BACKGROUND Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD). OBJECTIVE In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images. METHODS A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions. The encoder architecture was based on the residual and inception modules to obtain multi-scale features from multiple convolutional layers with different window shapes. Transfer learning was utilized to improve both the initial performance and learning efficiency. A hybrid loss function was employed to further optimize the segmentation model. RESULTS The model was tested on a data set of 616 ICAs obtained from 210 patients, composed of 437 images for training, 49 images for validation, and 130 images for testing. The segmentation model achieved a Dice score of 0.8942, a sensitivity of 0.8735, a specificity of 0.9954, and a Hausdorff distance of 6.0794 mm; it could predict arteries for a single ICA frame in 0.2114 seconds. CONCLUSIONS The results showed that our model outperformed the state-of-the-art deep-learning models. Our new method has great potential for clinical use.
Collapse
Affiliation(s)
- Yinghui Meng
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
| | - Zhenglong Du
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
| | - Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, USA
| | - Minghao Dong
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
| | - Drew Pienta
- Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, USA
| | - Jinshan Tang
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, USA
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, Michigan Technological University, Houghton, MI, USA
- Health Research Institute, Michigan Technological University, Houghton, MI, USA
| |
Collapse
|
5
|
Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
Collapse
Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| |
Collapse
|
6
|
Zhao Y, Wang Y, Zhang J, Liu X, Li Y, Guo S, Yang X, Hong S. Surgical GAN: Towards real-time path planning for passive flexible tools in endovascular surgeries. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
7
|
Classification of Left and Right Coronary Arteries in Coronary Angiographies Using Deep Learning. ELECTRONICS 2022. [DOI: 10.3390/electronics11132087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.
Collapse
|
8
|
Zhao C, Tang H, McGonigle D, He Z, Zhang C, Wang YP, Deng HW, Bober R, Zhou W. Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms. J Med Imaging (Bellingham) 2022; 9:044002. [PMID: 35875389 PMCID: PMC9295705 DOI: 10.1117/1.jmi.9.4.044002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, developing accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary. Approach: A multi-input and multiscale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. The proposed model generated a refined prediction map with the following two training stages: (i) stage I coarsely segmented the major coronary arteries from preprocessed single-channel ICAs and generated the probability map of arteries; and (ii) during the stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation result. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Results: Experimental results demonstrated that the proposed method achieved an average Dice similarity coefficient of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patients. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043. Conclusions: Our proposed approach has great promise for clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD.
Collapse
Affiliation(s)
- Chen Zhao
- Michigan Technological University, Department of Applied Computing, Houghton, Michigan, United States
| | - Haipeng Tang
- University of Southern Mississippi, School of Computing Sciences and Computer Engineering, Hattiesburg, Mississippi, United States
| | - Daniel McGonigle
- University of Southern Mississippi, School of Computing Sciences and Computer Engineering, Hattiesburg, Mississippi, United States
| | - Zhuo He
- Michigan Technological University, Department of Applied Computing, Houghton, Michigan, United States
| | - Chaoyang Zhang
- University of Southern Mississippi, School of Computing Sciences and Computer Engineering, Hattiesburg, Mississippi, United States
| | - Yu-Ping Wang
- Tulane University School of Public Health and Tropical Medicine, Tulane Center of Bioinformatics and Genomics, New Orleans, Louisiana, United States
| | - Hong-Wen Deng
- Tulane University School of Public Health and Tropical Medicine, Tulane Center of Bioinformatics and Genomics, New Orleans, Louisiana, United States
| | - Robert Bober
- Ochsner Medical Center, Department of Cardiology, New Orleans, Louisiana, United States
| | - Weihua Zhou
- Michigan Technological University, Department of Applied Computing, Houghton, Michigan, United States
- Michigan Technological University, Institute of Computing and Cybersystems, and Health Research Institute, Center of Biocomputing and Digital Health, Houghton, Michigan, United States
| |
Collapse
|
9
|
Han T, Ai D, Wang Y, Bian Y, An R, Fan J, Song H, Xie H, Yang J. Recursive Centerline- and Direction-Aware Joint Learning Network with Ensemble Strategy for Vessel Segmentation in X-ray Angiography Images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106787. [PMID: 35436660 DOI: 10.1016/j.cmpb.2022.106787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 03/05/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic vessel segmentation from X-ray angiography images is an important research topic for the diagnosis and treatment of cardiovascular disease. The main challenge is how to extract continuous and completed vessel structures from XRA images with poor quality and high complexity. Most existing methods predominantly focus on pixel-wise segmentation and overlook the geometric features, resulting in breaking and absence in segmentation results. To improve the completeness and accuracy of vessel segmentation, we propose a recursive joint learning network embedded with geometric features. METHODS The network joins the centerline- and direction-aware auxiliary tasks with the primary task of segmentation, which guides the network to explore the geometric features of vessel connectivity. Moreover, the recursive learning strategy is designed by passing the previous segmentation result into the same network iteratively to improve segmentation. To further enhance connectivity, we present a complementary-task ensemble strategy by fusing the outputs of the three tasks for the final segmentation result with majority voting. RESULTS To validate the effectiveness of our method, we conduct qualitative and quantitative experiments on the XRA images of the coronary artery and aorta including aortic arch, thoracic aorta, and abdominal aorta. Our method achieves F1 scores of 85.61±3.48% for the coronary artery, 89.02±2.89% for the aortic arch, 88.22±3.33% for the thoracic aorta, and 83.12±4.61% for the abdominal aorta. CONCLUSIONS Compared with six state-of-the-art methods, our method shows the most complete and accurate vessel segmentation results.
Collapse
Affiliation(s)
- Tao Han
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Danni Ai
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Yonglin Bian
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Ruirui An
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingfan Fan
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Hongzhi Xie
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Jian Yang
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| |
Collapse
|
10
|
Tao X, Dang H, Zhou X, Xu X, Xiong D. A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms. Front Public Health 2022; 10:892418. [PMID: 35692314 PMCID: PMC9174536 DOI: 10.3389/fpubh.2022.892418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/04/2022] [Indexed: 11/28/2022] Open
Abstract
An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network.
Collapse
Affiliation(s)
- Xingxiang Tao
- School of Modern Posts/Automation, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hao Dang
- School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaoguang Zhou
- School of Modern Posts/Automation, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiangdong Xu
- Department of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medical and Health Sciences, Shanghai, China
| | - Danqun Xiong
- Department of Cardiology, Jiading District Central Hospital Affiliated Shanghai University of Medical and Health Sciences, Shanghai, China
| |
Collapse
|
11
|
He Y, Northrup H, Le H, Cheung AK, Berceli SA, Shiu YT. Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases. Front Bioeng Biotechnol 2022; 10:855791. [PMID: 35573253 PMCID: PMC9091352 DOI: 10.3389/fbioe.2022.855791] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 04/08/2022] [Indexed: 01/17/2023] Open
Abstract
Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.
Collapse
Affiliation(s)
- Yong He
- Division of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States
| | - Hannah Northrup
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
| | - Ha Le
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
| | - Alfred K. Cheung
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
- Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, United States
| | - Scott A. Berceli
- Division of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States
- Vascular Surgery Section, Malcom Randall Veterans Affairs Medical Center, Gainesville, FL, United States
| | - Yan Tin Shiu
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
- Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, United States
- *Correspondence: Yan Tin Shiu,
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Liu Y, Zhang X, Wan W, Liu S, Liu Y, Liu H, Zeng X, Zhang Q. Two new stenosis detection methods of coronary angiograms. Int J Comput Assist Radiol Surg 2022; 17:521-530. [PMID: 35028887 DOI: 10.1007/s11548-021-02551-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/21/2021] [Indexed: 11/05/2022]
Abstract
PURPOSE Coronary angiography is the "gold standard" for diagnosing coronary artery disease. At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. METHODS Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this method, the user can choose any vessel segment to do further analysis of its stenoses. RESULTS Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and [Formula: see text] score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality. CONCLUSION The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.
Collapse
Affiliation(s)
- Yaofang Liu
- School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China
| | - Xinyue Zhang
- School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China
| | - Wenlong Wan
- School of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, China
| | - Shaoyu Liu
- School of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, China
| | - Yingdi Liu
- School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China
| | - Hu Liu
- School of Material Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Xueying Zeng
- School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China
| | - Qing Zhang
- Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| |
Collapse
|
14
|
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.
Collapse
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
| | | | | |
Collapse
|
15
|
Zhao C, Vij A, Malhotra S, Tang J, Tang H, Pienta D, Xu Z, Zhou W. Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms. Comput Biol Med 2021; 136:104667. [PMID: 34315031 DOI: 10.1016/j.compbiomed.2021.104667] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 07/13/2021] [Accepted: 07/17/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Coronary artery disease (CAD) is the leading cause of death in the United States (US) and a major contributor to healthcare cost. Accurate segmentation of coronary arteries and detection of stenosis from invasive coronary angiography (ICA) are crucial in clinical decision making. PURPOSE We aim to develop an automatic method to extract coronary arteries by deep learning and detect arterial stenosis from ICAs. METHODS In this study, a deep learning model which integrates a feature pyramid with a U-Net++ model was developed to automatically segment coronary arteries in ICAs. A compound loss function which contains Dice loss, dilated Dice loss, and L2 regularization was utilized to train the proposed segmentation model. Following the segmentation, an algorithm which extracts vascular centerlines, calculates the diameters, and measures the stenotic levels, was developed to detect arterial stenosis. RESULTS AND CONCLUSIONS In the dataset consisting of 314 ICAs obtained from 99 patients, the segmentation model achieved an average Dice score of 0.8899, a sensitivity of 0.8595, and a specificity of 0.9960. In addition, the stenosis detection algorithm achieved a true positive rate of 0.6840 and a positive predictive value of 0.6998 on all types of stenosis, which has great promise to advance to clinical uses and could provide auxiliary suggestions for CAD diagnosis and treatment.
Collapse
Affiliation(s)
- Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA
| | - Aviral Vij
- Division of Cardiology, Cook County Health and Hospitals System, Chicago, IL, 60612, USA; Division of Cardiology, Rush Medical College, Chicago, IL, 60612, USA
| | - Saurabh Malhotra
- Division of Cardiology, Cook County Health and Hospitals System, Chicago, IL, 60612, USA; Division of Cardiology, Rush Medical College, Chicago, IL, 60612, USA
| | - Jinshan Tang
- Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA; Center of Biocomputing and Digital Health, Michigan Technological University, Houghton, MI, 49931, USA
| | - Haipeng Tang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, 39406, USA
| | - Drew Pienta
- Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, 49931, USA
| | - Zhihui Xu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China.
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA; Center of Biocomputing and Digital Health, Michigan Technological University, Houghton, MI, 49931, USA.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Danilov VV, Klyshnikov KY, Gerget OM, Kutikhin AG, Ganyukov VI, Frangi AF, Ovcharenko EA. Real-time coronary artery stenosis detection based on modern neural networks. Sci Rep 2021; 11:7582. [PMID: 33828165 PMCID: PMC8027436 DOI: 10.1038/s41598-021-87174-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/24/2021] [Indexed: 01/10/2023] Open
Abstract
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.
Collapse
Affiliation(s)
| | - Kirill Yu Klyshnikov
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | | | - Anton G Kutikhin
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Vladimir I Ganyukov
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | | | - Evgeny A Ovcharenko
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| |
Collapse
|
18
|
Fu J, Li W, Du J, Huang Y. A multiscale residual pyramid attention network for medical image fusion. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102488] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
19
|
Jia D, Zhuang X. Learning-based algorithms for vessel tracking: A review. Comput Med Imaging Graph 2021; 89:101840. [PMID: 33548822 DOI: 10.1016/j.compmedimag.2020.101840] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 10/07/2020] [Accepted: 12/03/2020] [Indexed: 11/24/2022]
Abstract
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.
Collapse
Affiliation(s)
- Dengqiang Jia
- School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
| |
Collapse
|
20
|
Moon JH, Lee DY, Cha WC, Chung MJ, Lee KS, Cho BH, Choi JH. Automatic stenosis recognition from coronary angiography using convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105819. [PMID: 33213972 DOI: 10.1016/j.cmpb.2020.105819] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. Coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. We propose a deep-learning algorithm that automatically recognizes stenosis in coronary angiographic images. METHODS The proposed method consists of key frame detection, deep learning model training for classification of stenosis on each key frame, and visualization of the possible location of the stenosis. Firstly, we propose an algorithm that automatically extracts key frames essential for diagnosis from 452 right coronary artery angiography movie clips. Our deep learning model is then trained with image-level annotations to classify the areas narrowed by over 50 %. To make the model focus on the salient features, we apply a self-attention mechanism. The stenotic locations are visualized using the activated area of feature maps with gradient-weighted class activation mapping. RESULTS The automatically detected key frame was very close to the manually selected key frame (average distance (1.70 ± 0.12) frame per clip). The model was trained with key frames on internal datasets, and validated with internal and external datasets. Our training method achieved high frame-wise area-under-the-curve of 0.971, frame-wise accuracy of 0.934, and clip-wise accuracy of 0.965 in the average values of cross-validation evaluations. The external validation results showed high performances with the mean frame-wise area-under-the-curve of (0.925 and 0.956) in the single and ensemble model, respectively. Heat map visualization shows the location for different types of stenosis in both internal and external data sets. With the self-attention mechanism, the stenosis could be precisely localized, which helps to accurately classify the stenosis by type. CONCLUSIONS Our automated classification algorithm could recognize and localize coronary artery stenosis highly accurately. Our approach might provide the basis for a screening and assistant tool for the interpretation of coronary angiography.
Collapse
Affiliation(s)
- Jong Hak Moon
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea.
| | - Da Young Lee
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea.
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
| | - Myung Jin Chung
- Medical AI Research Center, Samsung Medical Center, Seoul 06351, South Korea; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
| | - Kyu-Sung Lee
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea; Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
| | - Baek Hwan Cho
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea; Medical AI Research Center, Samsung Medical Center, Seoul 06351, South Korea.
| | - Jin Ho Choi
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
| |
Collapse
|
21
|
Samuel PM, Veeramalai T. VSSC Net: Vessel Specific Skip chain Convolutional Network for blood vessel segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105769. [PMID: 33039919 DOI: 10.1016/j.cmpb.2020.105769] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning techniques are instrumental in developing network models that aid in the early diagnosis of life-threatening diseases. To screen and diagnose the retinal fundus and coronary blood vessel disorders, the most important step is the proper segmentation of the blood vessels. METHODS This paper aims to segment the blood vessels from both the coronary angiogram and the retinal fundus images using a single VSSC Net after performing the image-specific preprocessing. The VSSC Net uses two-vessel extraction layers with added supervision on top of the base VGG-16 network. The vessel extraction layers comprise of the vessel-specific convolutional blocks to localize the blood vessels, skip chain convolutional layers to enable rich feature propagation, and a unique feature map summation. Supervision is associated with the two-vessel extraction layers using separate loss/sigmoid function. Finally, the weighted fusion of the individual loss/sigmoid function produces the desired blood vessel probability map. It is then binary segmented and validated for performance. RESULTS The VSSC Net shows improved accuracy values on the standard retinal and coronary angiogram datasets respectively. The computational time required to segment the blood vessels is 0.2 seconds using GPU. Moreover, the vessel extraction layer uses a lesser parameter count of 0.4 million parameters to accurately segment the blood vessels. CONCLUSION The proposed VSSC Net that segments blood vessels from both the retinal fundus images and coronary angiogram can be used for the early diagnosis of vessel disorders. Moreover, it could aid the physician to analyze the blood vessel structure of images obtained from multiple imaging sources.
Collapse
Affiliation(s)
- Pearl Mary Samuel
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
| | | |
Collapse
|
22
|
Chen L, Sun J, Canton G, Balu N, Hippe DS, Zhao X, Li R, Hatsukami TS, Hwang JN, Yuan C. Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:217603-217614. [PMID: 33777593 PMCID: PMC7996631 DOI: 10.1109/access.2020.3040616] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.
Collapse
Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Jie Sun
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Gador Canton
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Xihai Zhao
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | - Rui Li
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | | | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
23
|
Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101978] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
24
|
Shukla AK, Pandey RK, Pachori RB. A fractional filter based efficient algorithm for retinal blood vessel segmentation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101883] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
25
|
Zhu J, Fan J, Guo S, Ai D, Song H, Wang C, Zhou S, Yang J. Heuristic tree searching for pose-independent 3D/2D rigid registration of vessel structures. ACTA ACUST UNITED AC 2020; 65:055010. [DOI: 10.1088/1361-6560/ab6b43] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
26
|
Prabhu SM, Chakiat A, S S, Vunnava KP, Shetty R. Deep learning segmentation and quantification of Meibomian glands. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101776] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
27
|
Cerebrovascular segmentation from TOF-MRA using model- and data-driven method via sparse labels. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.092] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
28
|
Pandey S, Singh PR, Tian J. An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101782] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
29
|
Segmentation through patch classification: A neural network approach to detect Posidonia oceanica in underwater images. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101053] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
30
|
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: 35] [Impact Index Per Article: 7.0] [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.
Collapse
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
| |
Collapse
|
31
|
Akbari M, Mohrekesh M, Nasr-Esfahani E, Soroushmehr SMR, Karimi N, Samavi S, Najarian K. Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:69-72. [PMID: 30440343 DOI: 10.1109/embc.2018.8512197] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we proposed a polyp segmentation method based on the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform effective post-processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.
Collapse
|
32
|
Hajabdollahi M, Esfandiarpoor R, Khadivi P, Soroushmehr S, Karimi N, Najarian K, Samavi S. Segmentation of bleeding regions in wireless capsule endoscopy for detection of informative frames. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101565] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
33
|
A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot. Med Biol Eng Comput 2019; 57:1875-1887. [DOI: 10.1007/s11517-019-02002-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 06/05/2019] [Indexed: 01/12/2023]
|
34
|
Qin B, Jin M, Hao D, Lv Y, Liu Q, Zhu Y, Ding S, Zhao J, Fei B. Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms. PATTERN RECOGNITION 2019; 87:38-54. [PMID: 31447490 PMCID: PMC6708416 DOI: 10.1016/j.patcog.2018.09.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.
Collapse
Affiliation(s)
- Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mingxin Jin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dongdong Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yisong Lv
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - 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
| | - Song Ding
- Department of Cardiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zhao
- School of Biomedical Engineering, 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
| |
Collapse
|
35
|
Zhang K, Zhang H, Zhou H, Crookes D, Li L, Shao Y, Liu D. Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:8214975. [PMID: 30863436 PMCID: PMC6378085 DOI: 10.1155/2019/8214975] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/14/2018] [Accepted: 12/31/2018] [Indexed: 11/17/2022]
Abstract
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.
Collapse
Affiliation(s)
- Kun Zhang
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Hongbin Zhang
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Huiyu Zhou
- Department of Informatics, University of Leicester, Leicester, UK
| | | | - Ling Li
- School of Computing, University of Kent, Canterbury, UK
| | - Yeqin Shao
- School of Transportation, Nantong University, Nantong 226019, China
| | - Dong Liu
- Co-innovation Center of Neuroregeneration, Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong 226001, China
| |
Collapse
|
36
|
Impact of Enhancement for Coronary Artery Segmentation Based on Deep Learning Neural Network. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1007/978-3-030-31321-0_23] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
37
|
Carballal A, Novoa FJ, Fernandez-Lozano C, García-Guimaraes M, Aldama-López G, Calviño-Santos R, Vazquez-Rodriguez JM, Pazos A. Automatic multiscale vascular image segmentation algorithm for coronary angiography. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.06.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|