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Zhu R, Li Q, Ding Z, Liu K, Lin Q, Yu Y, Li Y, Zhou S, Kuang H, Jiang J, Liu T. Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning. Phys Med Biol 2024; 69:155009. [PMID: 38981596 DOI: 10.1088/1361-6560/ad611c] [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: 04/17/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT.Approach. Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model.Main results. By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305 mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance section is 0.1 and 0.2 mm, respectively.Significance. The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.
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Affiliation(s)
- Rongyang Zhu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Qingrui Li
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Zhenyang Ding
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Kun Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Qiutong Lin
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Yin Yu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Yuanyao Li
- Tianjin Institute of Metrological Supervision and Testing, Tianjin 300192, People's Republic of China
| | - Shanshan Zhou
- Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, People's Republic of China
| | - Hao Kuang
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu 210093, People's Republic of China
| | - Junfeng Jiang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
| | - Tiegen Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin 300072, People's Republic of China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072, People's Republic of China
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Haft-Javaherian M, Villiger M, Otsuka K, Daemen J, Libby P, Golland P, Bouma BE. Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses. BIOMEDICAL OPTICS EXPRESS 2024; 15:1719-1738. [PMID: 38495711 PMCID: PMC10942710 DOI: 10.1364/boe.514673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 03/19/2024]
Abstract
Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.
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Affiliation(s)
- Mohammad Haft-Javaherian
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Martin Villiger
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kenichiro Otsuka
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Joost Daemen
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter Libby
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Brett E. Bouma
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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Wu W, Roby M, Banga A, Oguz UM, Gadamidi VK, Hasini Vasa C, Zhao S, Dasari VS, Thota AK, Tanweer S, Lee C, Kassab GS, Chatzizisis YS. Rapid automated lumen segmentation of coronary optical coherence tomography images followed by 3D reconstruction of coronary arteries. J Med Imaging (Bellingham) 2024; 11:014004. [PMID: 38173655 PMCID: PMC10760146 DOI: 10.1117/1.jmi.11.1.014004] [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: 06/19/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose Optical coherence tomography has emerged as an important intracoronary imaging technique for coronary artery disease diagnosis as it produces high-resolution cross-sectional images of luminal and plaque morphology. Precise and fast lumen segmentation is essential for efficient OCT morphometric analysis. However, due to the presence of various image artifacts, including side branches, luminal blood artifacts, and complicated lesions, this remains a challenging task. Approach Our research study proposes a rapid automatic segmentation method that utilizes nonuniform rational B-spline to connect limited pixel points and identify the edges of the OCT lumen. The proposed method suppresses image noise and accurately extracts the lumen border with a high correlation to ground truth images based on the area, minimal diameter, and maximal diameter. Results We evaluated the method using 3300 OCT frames from 10 patients and found that it achieved favorable results. The average time taken for automatic segmentation by the proposed method is 0.17 s per frame. Additionally, the proposed method includes seamless vessel reconstruction following the lumen segmentation. Conclusions The developed automated system provides an accurate, efficient, robust, and user-friendly platform for coronary lumen segmentation and reconstruction, which can pave the way for improved assessment of the coronary artery lumen morphology.
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Affiliation(s)
- Wei Wu
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Merjulah Roby
- The University of Texas San Antonio, Department of Mechanical Engineering, Vascular Biomechanics and Biofluids, San Antonio, Texas, United States
| | - Akshat Banga
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Usama M. Oguz
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Vinay Kumar Gadamidi
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Charu Hasini Vasa
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Shijia Zhao
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Vineeth S. Dasari
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Anjani Kumar Thota
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Sartaj Tanweer
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Changkye Lee
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Ghassan S. Kassab
- California Medical Innovation Institute, San Diego, California, United States
| | - Yiannis S. Chatzizisis
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
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Zhu F, Yu Y, Ding Z, Li Q, Zhou S, Tao K, Kuang H, Liu T. Automatic bifurcation detection utilizing pullback characteristics of bifurcation in intravascular optical coherence tomography. OPTICS EXPRESS 2022; 30:31381-31395. [PMID: 36242221 DOI: 10.1364/oe.466258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
Bifurcation detection in coronary arteries is significant since it influences the treatment strategy selection and optimization. Bifurcations are also reliable landmarks for image registration. Intravascular optical coherence tomography (IVOCT) is a high-resolution imaging modality that is very useful in percutaneous coronary intervention stenting optimization. We present a bifurcation identification method utilizing pullback characteristics for IVOCT, which can effectively identify the bifurcations with a small size. The longitudinal view of the pullback will appear as an outward discontinuity in the bifurcation area. By detecting this discontinuity, bifurcation can be identified with high accuracy. We also use the normal vectors method to extract the ostium of bifurcation. We compare the proposed method with the widely-used distance transformation method by clinical 5302 IVOCT images from 22 pullbacks. The average metrics of true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) for the proposed method are 86.97%, 98.50%, 85.56%, and 98.67%, respectively. TPR, PPV, and NPV by the proposed method are improved by 40.24%, 9.31%, 3.90%, and TNR is on par compared with the distance transformation method. Especially in the small bifurcation identification, TPR of the proposed method is 64.71% higher than the distance transformation method with a bifurcation area ratio less than 0.2.
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Carpenter HJ, Ghayesh MH, Zander AC, Li J, Di Giovanni G, Psaltis PJ. Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction. Tomography 2022; 8:1307-1349. [PMID: 35645394 PMCID: PMC9149962 DOI: 10.3390/tomography8030108] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.
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Affiliation(s)
- Harry J. Carpenter
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Correspondence: (H.J.C.); (M.H.G.)
| | - Mergen H. Ghayesh
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Correspondence: (H.J.C.); (M.H.G.)
| | - Anthony C. Zander
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Jiawen Li
- School of Electrical Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, SA 5005, Australia
- Institute for Photonics and Advanced Sensing, University of Adelaide, Adelaide, SA 5005, Australia
| | - Giuseppe Di Giovanni
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
| | - Peter J. Psaltis
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
- Department of Cardiology, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia
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Zhang R, Fan Y, Qi W, Wang A, Tang X, Gao T. Current research and future prospects of IVOCT imaging-based detection of the vascular lumen and vulnerable plaque. JOURNAL OF BIOPHOTONICS 2022; 15:e202100376. [PMID: 35139263 DOI: 10.1002/jbio.202100376] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars.
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Affiliation(s)
- Ruolin Zhang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Wenliu Qi
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Ancong Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, Beijing, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing, China
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Zhu F, Ding Z, Tao K, Li Q, Kuang H, Tian F, Zhou S, Hua P, Hu J, Shang M, Yu Y, Liu T. Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography. JOURNAL OF BIOPHOTONICS 2021; 14:e202100124. [PMID: 34185435 DOI: 10.1002/jbio.202100124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/25/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A-lines, we detect the A-lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts.
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Affiliation(s)
- Fengyu Zhu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Zhenyang Ding
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Kuiyuan Tao
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Qingrui Li
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Hao Kuang
- Nanjing Forssmann Medical Technology Co., Nanjing, China
| | - Feng Tian
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Shanshan Zhou
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Peidong Hua
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Jingqi Hu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Mingjian Shang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Yin Yu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Tiegen Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
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Balaji A, Kelsey LJ, Majeed K, Schultz CJ, Doyle BJ. Coronary artery segmentation from intravascular optical coherence tomography using deep capsules. Artif Intell Med 2021; 116:102072. [PMID: 34020750 DOI: 10.1016/j.artmed.2021.102072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/20/2022]
Abstract
The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. Current image processing methods are hindered by the time needed to generate expert-labelled datasets and the potential for bias during the analysis. Therefore, automated, robust, unbiased and timely geometry extraction from IVOCT, using image processing, would be beneficial to clinicians. With clinical application in mind, we aim to develop a model with a small memory footprint that is fast at inference time without sacrificing segmentation quality. Using a large IVOCT dataset of 12,011 expert-labelled images from 22 patients, we construct a new deep learning method based on capsules which automatically produces lumen segmentations. Our dataset contains images with both blood and light artefacts (22.8 %), as well as metallic (23.1 %) and bioresorbable stents (2.5 %). We split the dataset into a training (70 %), validation (20 %) and test (10 %) set and rigorously investigate design variations with respect to upsampling regimes and input selection. We show that our developments lead to a model, DeepCap, that is on par with state-of-the-art machine learning methods in terms of segmentation quality and robustness, while using as little as 12 % of the parameters. This enables DeepCap to have per image inference times up to 70 % faster on GPU and up to 95 % faster on CPU compared to other state-of-the-art models. DeepCap is a robust automated segmentation tool that can aid clinicians to extract unbiased geometrical data from IVOCT.
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Affiliation(s)
- Arjun Balaji
- Vascular Engineering Laboratory, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | - Lachlan J Kelsey
- Vascular Engineering Laboratory, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Australia; School of Engineering, The University of Western Australia, Perth, Australia
| | - Kamran Majeed
- Department of Cardiology, Royal Perth Hospital, Perth, Australia; School of Medicine, The University of Western Australia, Perth, Australia; University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Carl J Schultz
- Department of Cardiology, Royal Perth Hospital, Perth, Australia; School of Medicine, The University of Western Australia, Perth, Australia
| | - Barry J Doyle
- Vascular Engineering Laboratory, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Australia; School of Engineering, The University of Western Australia, Perth, Australia; Australian Research Council Centre for Personalised Therapeutics Technologies, Australia; British Heart Foundation Centre for Cardiovascular Science, The University of Edinburgh, UK.
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Miyagawa M, Costa MGF, Gutierrez MA, Costa JPGF, Costa Filho CFF. Using Convolutional Neural Networks for Classification of Bifurcation Regions in IVOCT Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5597-5600. [PMID: 31947124 DOI: 10.1109/embc.2019.8857371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Optical Coherence Tomography (OCT) technology enabled the experts to analyze coronary lesions from high-resolution intravascular images. Studies have shown the relationship between bifurcation regions and a higher occurrence of wall thickening and lesions in these areas. Some level of automation could benefit experts, since examining pullback frames is a laborious and time-consuming task. Although Convolutional Neural Networks (CNN) have shown promising results in classification tasks of medical images, we did not identify the use of CNN's in IVOCT images to classify bifurcation regions in the literature. In this work, we evaluated a CNN architecture in the bifurcation classification task trained with IVOCT images from 9 pullbacks from 9 different patients. We used data augmentation to balance the dataset, due to the low amount of bifurcation-labeled frames. Our classification results are comparable to other works in the literature, presenting better result in AUC (99.70%).
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Miyagawa M, Costa MGF, Gutierrez MA, Costa JPGF, Filho CFFC. Lumen Segmentation in Optical Coherence Tomography Images using Convolutional Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:600-603. [PMID: 30440468 DOI: 10.1109/embc.2018.8512299] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Lumen segmentation in Optical Coherence Tomography (OCT) images is a very important step to analyze points of interest that may help on atherosclerosis diagnostic and treatment. Past studies use many different methods to segment the lumen in IVOCT images, like level set, morphological reconstruction, Markov random fields, and Otsu binarization. Despite Convolutional Neural Networks (CNN) have shown promising results in the image processing area, we did not identify, in the literature, works applying CNN in IVOCT images. In this paper, we present the lumen segmentation using CNN. We evaluated three different CNN architectures. The CNNs were evaluated using three versions from the image dataset, differing from each other by image size (768x768 pixels and 192x192 pixels), and by coordinate system representation (Cartesian and polar). The best results, Accuracy, Dice index and Jaccard index of over 99%, 98% and 97%, respectively, were obtained with the smallest size images represented by polar coordinate system.
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Joseph S, Adnan A, Adlam D. Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method. J Med Imaging (Bellingham) 2016; 3:044001. [PMID: 27981064 DOI: 10.1117/1.jmi.3.4.044001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 11/01/2016] [Indexed: 11/14/2022] Open
Abstract
Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three-dimensional perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artifacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance-based lumen intensity enhancement method that ensures only lumen regions are highlighted. A level-set-based active contour method that utilizes the local speckle distribution properties of the image is then employed to drive an image-specific active contour toward the true lumen boundaries. By utilizing local speckle properties, the intensity variation issues within the image are resolved. This combined approach has been successfully applied to challenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow, and its shadowing artifact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can be performed even when guide-wire boundaries are not easily identified. Lumen enhancement also makes it easy to identify vessel side branches. This automated approach is not only able to extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa vasorum and with atherosclerotic plaque.
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Affiliation(s)
- Shiju Joseph
- University of Leicester , Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield General Hospital, Leicester LE3 9QP, United Kingdom
| | - Asif Adnan
- University of Leicester , Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield General Hospital, Leicester LE3 9QP, United Kingdom
| | - David Adlam
- University of Leicester , Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield General Hospital, Leicester LE3 9QP, United Kingdom
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