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Zhou Q, Tan W, Li Q, Li B, Zhou L, Liu X, Yang J, Zhao D. A new segment method for pulmonary artery and vein. Health Inf Sci Syst 2023; 11:47. [PMID: 37810417 PMCID: PMC10558422 DOI: 10.1007/s13755-023-00245-8] [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: 06/01/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023] Open
Abstract
Accurate differentiation between pulmonary arteries and veins (A/V) holds pivotal importance in the realm of diagnosing and treating pulmonary ailments. This study presents a new approach that leverages grayscale differences between A/V. Distinctions are measured using median and mean grayscale values within the vessel area. Initially, adherent regions are removed based on vessel structure. The trunk regions are segmented using gray level information near the heart region of the lung boundary. Incorrectly segmented vessels are corrected based on connectivity. For distal lung vessels, a similar distance field is established using a graph-cut method. Experimental results show the algorithm's superior segmentation accuracy, achieving 97.26% compared to the CNN-based average accuracy of 91.67%. Error branches are more concentrated, aiding subsequent manual and automatic correction. This demonstrates the algorithm's effective segmentation of pulmonary A/V.
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Affiliation(s)
- Qinghua Zhou
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Qingya Li
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Baoting Li
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Luyu Zhou
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Xin Liu
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Dazhe Zhao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
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2
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Zeng A, Wu C, Lin G, Xie W, Hong J, Huang M, Zhuang J, Bi S, Pan D, Ullah N, Khan KN, Wang T, Shi Y, Li X, Xu X. ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comput Med Imaging Graph 2023; 109:102287. [PMID: 37634975 DOI: 10.1016/j.compmedimag.2023.102287] [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: 01/15/2023] [Revised: 05/03/2023] [Accepted: 08/03/2023] [Indexed: 08/29/2023]
Abstract
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.
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Affiliation(s)
- An Zeng
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Chunbiao Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Guisen Lin
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Wen Xie
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jin Hong
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Meiping Huang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jian Zhuang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Shanshan Bi
- Department of Computer Science and Engineering, Missouri University of Science and Technology, Rolla, MO, United States
| | - Dan Pan
- Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Najeeb Ullah
- Department of Computer Science, University of Engineering and Technology, Mardan, KP, Pakistan
| | - Kaleem Nawaz Khan
- Department of Computer Science, University of Engineering and Technology, Mardan, KP, Pakistan
| | - Tianchen Wang
- Department of Computer Science and Engineering, University of Notre Dame, Indiana, United States
| | - Yiyu Shi
- Department of Computer Science and Engineering, University of Notre Dame, Indiana, United States
| | - Xiaomeng Li
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
| | - Xiaowei Xu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
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3
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Gharleghi R, Chen N, Sowmya A, Beier S. Towards automated coronary artery segmentation: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107015. [PMID: 35914439 DOI: 10.1016/j.cmpb.2022.107015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/03/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Vessel segmentation is the first processing stage of 3D medical images for both clinical and research use. Current segmentation methods are tedious and time consuming, requiring significant manual correction and hence are infeasible to use in large data sets. METHODS Here, we review and analyse available coronary artery segmentation methods, focusing on fully automated methods capable of handling the rapidly growing medical images available. All manuscripts published since 2010 are systematically reviewed, categorised into different groups based on the approach taken, and characteristics of the different approaches as well as trends over the past decade are explored. RESULTS The manuscripts were divided intro three broad categories, consisting of region growing, voxelwise prediction and partitioning approaches. The most common approach overall was region growing, particularly using active contour models, however these have had a sharp fall in popularity in recent years with convolutional neural networks becoming significantly more popular. CONCLUSIONS The systematic review of current coronary artery segmentation methods shows interesting trends, with rising popularity of machine learning methods, a focus on efficient methods, and falling popularity of computationally expensive processing steps such as vesselness and multiplanar reformation.
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Affiliation(s)
- Ramtin Gharleghi
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia.
| | - Nanway Chen
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, UNSW, Sydney NSW 2053, Australia; Tyree Foundation Institute of Health Engineering (Tyree IHealthE), Sydney, Australia
| | - Susann Beier
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia
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Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors. Diagnostics (Basel) 2022; 12:diagnostics12081987. [PMID: 36010337 PMCID: PMC9406865 DOI: 10.3390/diagnostics12081987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.
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Huang Y, Yang J, Sun Q, Ma S, Yuan Y, Tan W, Cao P, Feng C. Vessel filtering and segmentation of coronary CT angiographic images. Int J Comput Assist Radiol Surg 2022; 17:1879-1890. [PMID: 35764765 DOI: 10.1007/s11548-022-02655-7] [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: 10/01/2021] [Accepted: 04/22/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images. METHOD In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images. RESULTS The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods. CONCLUSION The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.
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Affiliation(s)
- Yan Huang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China. .,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Qi Sun
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Shuang Ma
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Yuliang Yuan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Peng Cao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
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Lee HJ, Kim YW, Kim JH, Lee YJ, Moon J, Jeong P, Jeong J, Kim JS, Lee JS. Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106827. [PMID: 35500505 DOI: 10.1016/j.cmpb.2022.106827] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/31/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. OBJECTIVES The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. METHOD The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. CONCLUSION Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization.
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Affiliation(s)
- Hyeong Jun Lee
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Young Woo Kim
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Jun Hong Kim
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Yong-Joon Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Korea
| | | | | | | | - Jung-Sun Kim
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Korea
| | - Joon Sang Lee
- School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea.
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Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7013703. [PMID: 35510177 PMCID: PMC9034947 DOI: 10.1155/2022/7013703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/23/2022] [Indexed: 11/17/2022]
Abstract
The diagnostic efficacy of coronary computed tomography angiography (CTA) images of coronary arteries in restenosis after coronary stenting based on the combination of the convolutional neural network (CNN) algorithm and the automatic segmentation algorithm for region growth of vascular similarity features was explored to provide a more effective diagnostic method for patients. 130 patients with coronary artery disease were randomly selected as the research objects, and they were averagely classified into the control group (conventional coronary CTA image diagnosis) and the observation group (coronary CTA image diagnosis based on an improved automatic segmentation algorithm). Based on the diagnostic criteria of coronary angiography (CAG), the efficacy of two kinds of coronary CTA images on the postoperative subsequent visit of coronary heart disease (CHD) stenting was evaluated. The results showed that the accuracy of the CNN algorithm was 87.89%, and the average voxel error of the improved algorithm was signally lower than that of the traditional algorithm (1.8921 HU/voxel vs. 7.10091 HU/voxel) (p < 0.05). The average score of the coronary CTA image in the observation group was higher than that in the control group (2.89 ± 0.11 points vs. 2.01 ± 0.73 points) (p < 0.05). The diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and accuracy (89.23%) of the observation group were higher than those of the control group (p < 0.05). In conclusion, the region growth algorithm under the CNN algorithm and vascular similarity features had an accurate segmentation effect, which was helpful for the diagnosis of CTA image in restenosis after coronary stenting.
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Song A, Xu L, Wang L, Wang B, Yang X, Xu B, Yang B, Greenwald SE. Automatic Coronary Artery Segmentation of CCTA Images with an Efficient Feature-Fusion-and-Rectification 3D-UNet. IEEE J Biomed Health Inform 2022; 26:4044-4055. [PMID: 35446776 DOI: 10.1109/jbhi.2022.3169425] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D_CAS.
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Cheung WK, Bell R, Nair A, Menezes LJ, Patel R, Wan S, Chou K, Chen J, Torii R, Davies RH, Moon JC, Alexander DC, Jacob J. A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:108873-108888. [PMID: 34395149 PMCID: PMC8357413 DOI: 10.1109/access.2021.3099030] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 06/26/2021] [Indexed: 06/13/2023]
Abstract
Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.
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Affiliation(s)
- Wing Keung Cheung
- Centre for Medical Image ComputingUniversity College LondonLondonWC1V 6LJU.K.
- Department of Computer ScienceUniversity College LondonLondonWC1V 6LJU.K.
| | - Robert Bell
- Hatter Cardiovascular Institute, University College LondonLondonWC1V 6LJU.K.
| | - Arjun Nair
- Department of RadiologyUniversity College London HospitalLondonNW1 2BUU.K.
| | - Leon J. Menezes
- Institute of Nuclear Medicine, University College LondonLondonWC1V 6LJU.K.
| | - Riyaz Patel
- Institute of Cardiovascular Science, University College LondonLondonWC1V 6LJU.K.
| | - Simon Wan
- Institute of Nuclear Medicine, University College LondonLondonWC1V 6LJU.K.
| | - Kacy Chou
- Centre for Medical Image ComputingUniversity College LondonLondonWC1V 6LJU.K.
- Department of Computer ScienceUniversity College LondonLondonWC1V 6LJU.K.
| | - Jiahang Chen
- Department of Mechanical EngineeringUniversity College LondonLondonWC1E 7JEU.K.
| | - Ryo Torii
- Department of Mechanical EngineeringUniversity College LondonLondonWC1E 7JEU.K.
| | - Rhodri H. Davies
- Institute of Cardiovascular Science, University College LondonLondonWC1V 6LJU.K.
- Barts Heart CentreLondonEC1A 7BEU.K.
| | - James C. Moon
- Institute of Cardiovascular Science, University College LondonLondonWC1V 6LJU.K.
- Barts Heart CentreLondonEC1A 7BEU.K.
| | - Daniel C. Alexander
- Centre for Medical Image ComputingUniversity College LondonLondonWC1V 6LJU.K.
- Department of Computer ScienceUniversity College LondonLondonWC1V 6LJU.K.
| | - Joseph Jacob
- Centre for Medical Image ComputingUniversity College LondonLondonWC1V 6LJU.K.
- Department of Respiratory MedicineUniversity College LondonLondonWC1V 6LJU.K.
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