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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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2
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Shi P, Xin J, Du S, Wu J, Deng Y, Cai Z, Zheng N. Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning. JOURNAL OF BIOPHOTONICS 2023; 16:e202300059. [PMID: 37289201 DOI: 10.1002/jbio.202300059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/12/2023] [Accepted: 06/01/2023] [Indexed: 06/09/2023]
Abstract
Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning-based methods usually require well-annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta-learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi-level gradient strategy to train a meta-learner for capturing the shared meta-knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw-type network and a contrast consistency loss were designed to better learn the meta-knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state-of-art performance.
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Affiliation(s)
- Peiwen Shi
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Jingmin Xin
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Shaoyi Du
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Jiayi Wu
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Yangyang Deng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
- Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Zhuotong Cai
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
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3
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Huang M, Maehara A, Tang D, Zhu J, Wang L, Lv R, Zhu Y, Zhang X, Matsumura M, Chen L, Ma G, Mintz GS. Comparison of multilayer and single-layer coronary plaque models on stress/strain calculations based on optical coherence tomography images. Front Physiol 2023; 14:1251401. [PMID: 37608838 PMCID: PMC10440539 DOI: 10.3389/fphys.2023.1251401] [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: 07/01/2023] [Accepted: 07/24/2023] [Indexed: 08/24/2023] Open
Abstract
Mechanical stress and strain conditions are closely related to atherosclerotic plaque progression and rupture and have been under intensive investigations in recent years. It is well known that arteries have a three-layer structure: intima, media and adventitia. However, in vivo image-based multilayer plaque models are not available in the current literature due to lack of multilayer image segmentation data. A multilayer segmentation and repairing technique was introduced to segment coronary plaque optical coherence tomography (OCT) image to obtain its three-layer vessel structure. A total of 200 OCT slices from 20 patients (13 male; 7 female) were used to construct multilayer and single-layer 3D thin-slice models to calculate plaque stress and strain and compare model differences. Our results indicated that the average maximum plaque stress values of 20 patients from multilayer and single-layer models were 385.13 ± 110.09 kPa and 270.91 ± 95.86 kPa, respectively. The relative difference was 42.2%, with single-layer stress serving as the base value. The average mean plaque stress values from multilayer and single-layer models were 129.59 ± 32.77 kPa and 93.27 ± 18.20 kPa, respectively, with a relative difference of 38.9%. The maximum and mean plaque strain values obtained from the multilayer models were 11.6% and 19.0% higher than those from the single-layer models. Similarly, the maximum and mean cap strains showed increases of 9.6% and 12.9% over those from the single-layer models. These findings suggest that use of multilayer models could improve plaque stress and strain calculation accuracy and may have large impact on plaque progression and vulnerability investigation and potential clinical applications. Further large-scale studies are needed to validate our findings.
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Affiliation(s)
- Mengde Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, United States
| | - Jian Zhu
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rui Lv
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yanwen Zhu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiaoguo Zhang
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
| | - Lijuan Chen
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Genshan Ma
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Gary S. Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
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4
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Huang M, Maehara A, Tang D, Zhu J, Wang L, Lv R, Zhu Y, Zhang X, Matsumura M, Chen L, Ma G, Mintz GS. Human Coronary Plaque Optical Coherence Tomography Image Repairing, Multilayer Segmentation and Impact on Plaque Stress/Strain Calculations. J Funct Biomater 2022; 13:jfb13040213. [PMID: 36412854 PMCID: PMC9680523 DOI: 10.3390/jfb13040213] [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: 09/25/2022] [Revised: 10/25/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Coronary vessel layer structure may have a considerable impact on plaque stress/strain calculations. Most current plaque models use single-layer vessel structures due to the lack of available multilayer segmentation techniques. In this paper, an automatic multilayer segmentation and repair method was developed to segment coronary optical coherence tomography (OCT) images to obtain multilayer vessel geometries for biomechanical model construction. Intravascular OCT data were acquired from six patients (one male; mean age: 70.0) using a protocol approved by the local institutional review board with informed consent obtained. A total of 436 OCT slices were selected in this study. Manually segmented data were used as the gold standard for method development and validation. The edge detection method and cubic spline surface fitting were applied to detect and repair the internal elastic membrane (IEM), external elastic membrane (EEM) and adventitia-periadventitia interface (ADV). The mean errors of automatic contours compared to manually segmented contours were 1.40%, 4.34% and 6.97%, respectively. The single-layer mean plaque stress value from lumen was 117.91 kPa, 10.79% lower than that from three-layer models (132.33 kPa). On the adventitia, the single-layer mean plaque stress value was 50.46 kPa, 156.28% higher than that from three-layer models (19.74 kPa). The proposed segmentation technique may have wide applications in vulnerable plaque research.
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Affiliation(s)
- Mengde Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10019, USA
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Jian Zhu
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Rui Lv
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yanwen Zhu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoguo Zhang
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10019, USA
| | - Lijuan Chen
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Genshan Ma
- Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Gary S Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10019, USA
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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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6
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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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7
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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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Subramaniam S, Jayanthi KB, Rajasekaran C, Sunder C. Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor
generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size
of the kernel as well as the number of kernels. Auto encoder–decoder performs pixel wise classification using two interconnected pathways for identifying the boundary of lumen-intima (LI) and media adventitia (MA). This helps in reconstruction of the segmented portion for measurement
of IMT. Both methods are tested using a dataset of 550 subjects. The results clearly indicate that end-to-end model has an edge over the pipeline model exhibiting lesser deviation between the automated measurement and the measurement made by the radiologist. The pipeline model however has
better segmentation accuracy when the size of the image used for training is small. The convolutional neural network with auto encoder–decoder proves robust through sparse representation, and faster learning with better generalization. Also, the experimental setup is analyzed by interconnecting
Tensor flow simulated result with Raspberry PI and the outcomes are analyzed.
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Affiliation(s)
- Sudha Subramaniam
- Research Scholar, Department of Electronics and Communication Engineering, K. S. Rangasamy College of Technology, Tiruchengode 637215, India
| | - K. B. Jayanthi
- School of Electrical Sciences, K. S. Rangasamy College of Technology, Tiruchengode 637215, India
| | - C. Rajasekaran
- Department of Electronics and Communication Engineering, K. S. Rangasamy College of Technology, Tiruchengode 637215, India
| | - C. Sunder
- Senior Consultant, Apollo Hospitals, Chennai 600081, India
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Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images.
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Gong P, Almasian M, van Soest G, de Bruin DM, van Leeuwen TG, Sampson DD, Faber DJ. Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-34. [PMID: 32246615 PMCID: PMC7118361 DOI: 10.1117/1.jbo.25.4.040901] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 02/28/2020] [Indexed: 05/07/2023]
Abstract
SIGNIFICANCE Optical coherence tomography (OCT) provides cross-sectional and volumetric images of backscattering from biological tissue that reveal the tissue morphology. The strength of the scattering, characterized by an attenuation coefficient, represents an alternative and complementary tissue optical property, which can be characterized by parametric imaging of the OCT attenuation coefficient. Over the last 15 years, a multitude of studies have been reported seeking to advance methods to determine the OCT attenuation coefficient and developing them toward clinical applications. AIM Our review provides an overview of the main models and methods, their assumptions and applicability, together with a survey of preclinical and clinical demonstrations and their translation potential. RESULTS The use of the attenuation coefficient, particularly when presented in the form of parametric en face images, is shown to be applicable in various medical fields. Most studies show the promise of the OCT attenuation coefficient in differentiating between tissues of clinical interest but vary widely in approach. CONCLUSIONS As a future step, a consensus on the model and method used for the determination of the attenuation coefficient is an important precursor to large-scale studies. With our review, we hope to provide a basis for discussion toward establishing this consensus.
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Affiliation(s)
- Peijun Gong
- The University of Western Australia, Department of Electrical, Electronic and Computer Engineering, Optical+Biomedical Engineering Laboratory, Perth, Western Australia, Australia
- Address all correspondence to Peijun Gong, E-mail:
| | - Mitra Almasian
- University of Amsterdam, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Gijs van Soest
- Erasmus MC, University Medical Center Rotterdam, Department of Cardiology, Rotterdam, The Netherlands
| | - Daniel M. de Bruin
- University of Amsterdam, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Ton G. van Leeuwen
- University of Amsterdam, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - David D. Sampson
- The University of Western Australia, Department of Electrical, Electronic and Computer Engineering, Optical+Biomedical Engineering Laboratory, Perth, Western Australia, Australia
- University of Surrey, Surrey Biophotonics, Guildford, Surrey, United Kingdom
| | - Dirk J. Faber
- University of Amsterdam, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
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11
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Yang S, Yoon HJ, Yazdi SJM, Lee JH. A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment. Int J Med Robot 2019; 16:e2033. [PMID: 31469940 DOI: 10.1002/rcs.2033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 08/12/2019] [Accepted: 08/24/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification. METHODS The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method. RESULTS As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively. CONCLUSIONS Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.
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Affiliation(s)
- Su Yang
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea
| | - Hyuck-Jun Yoon
- Department of Internal Medicine, School of Medicine, Keimyung University, Daegu, South Korea
| | | | - Jong-Ha Lee
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea
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12
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Rico-Jimenez JJ, Campos-Delgado DU, Buja LM, Vela D, Jo JA. Intravascular optical coherence tomography method for automated detection of macrophage infiltration within atherosclerotic coronary plaques. Atherosclerosis 2019; 290:94-102. [PMID: 31604172 DOI: 10.1016/j.atherosclerosis.2019.09.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/22/2019] [Accepted: 09/27/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS Significant macrophages infiltration in advanced atherosclerotic plaques promotes acute coronary events. Hence, the clinical imaging of macrophage content in coronary atherosclerotic plaques could potentially aid in identifying patients most at risk of future acute coronary events. The aim of this study was to introduce and validate a simple intravascular optical coherence tomography (IV-OCT) image processing method for automated, accurate and fast detection of macrophage infiltration within coronary atherosclerotic plaques. METHODS This method calculates the ratio of the normalized-intensity standard deviation (NSD) values estimated over two axially-adjacent regions of interest in an IV-OCT cross-sectional image (B-scan). When applied to entire IV-OCT B-scans, this method highlights plaque areas with high NSD ratio values (NSDRatio), which was demonstrated to be correlated with the degree of coronary plaque macrophage infiltration. RESULTS Using an optimized NSDRatio threshold value, coronary plaque macrophage infiltration could be detected with ~88% sensitivity and specificity in a database of 28 IV-OCT scans from postmortem coronary segments. For comparison, using an optimized NSD threshold value, considered the standard IV-OCT signature for macrophages, coronary plaque macrophage infiltration could be detected with only ~55% sensitivity and specificity. CONCLUSIONS The proposed NSDRatio method significantly increases the sensitivity and specificity for the detection of coronary plaque macrophage infiltration compared to the standard NSD method. This computationally efficient method can be seamlessly implemented within standard IV-OCT imaging systems for in-vivo real-time imaging of macrophage content in coronary plaques, which could potentially aid in identifying patients most at risk of future acute coronary events.
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Affiliation(s)
- Jose J Rico-Jimenez
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | | | | | | | - Javier A Jo
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
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13
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Olender ML, Athanasiou LS, de la Torre Hernández JM, Ben-Assa E, Nezami FR, Edelman ER. A Mechanical Approach for Smooth Surface Fitting to Delineate Vessel Walls in Optical Coherence Tomography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1384-1397. [PMID: 30507499 PMCID: PMC6541545 DOI: 10.1109/tmi.2018.2884142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Automated analysis of vascular imaging techniques is limited by the inability to precisely determine arterial borders. Intravascular optical coherence tomography (OCT) offers unprecedented detail of artery wall structure and composition, but does not provide consistent visibility of the outer border of the vessel due to the limited penetration depth. Existing interpolation and surface fitting methods prove insufficient to accurately fill the gaps between the irregularly spaced and sometimes unreliably identified visible segments of the vessel outer border. This paper describes an intuitive, efficient, and flexible new method of 3D surface fitting and smoothing suitable for this task. An anisotropic linear-elastic mesh is fit to irregularly spaced and uncertain data points corresponding to visible segments of vessel borders, enabling the fully automated delineation of the entire inner and outer borders of diseased vessels in OCT images for the first time. In a clinical dataset, the proposed smooth surface fitting approach had great agreement when compared with human annotations: areas differed by just 11 ± 11% (0.93 ± 0.84 mm2), with a coefficient of determination of 0.89. Overlapping and non-overlapping area ratios were 0.91 and 0.18, respectively, with a sensitivity of 90.8 and specificity of 99.0. This spring mesh method of contour fitting significantly outperformed all alternative surface fitting and interpolation approaches tested. The application of this promising proposed method is expected to enhance clinical intervention and translational research using OCT.
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Affiliation(s)
- Max L. Olender
- Institute for Medical Engineering and Science,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Department of Mechanical Engineering, Massachusetts
Institute of Technology, Cambridge, MA 02139 USA
| | - Lambros S. Athanasiou
- Institute for Medical Engineering and Science,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Brigham and Women’s Hospital, Harvard Medical
School, Cardiovascular Division, Boston, MA 02115 USA
| | - José M. de la Torre Hernández
- Hospital Universitario Marqués de Valdecilla, Unidad
de Cardiología Intervencionista, Servicio de Cardiología, IDIVAL,
39008 Santander, Spain
| | - Eyal Ben-Assa
- Institute for Medical Engineering and Science,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Massachusetts General Hospital, Harvard Medical School,
Cardiology Division, Department of Medicine, Boston, MA 02114 USA
- Tel-Aviv Sourasky Medical Center, Sackler Faculty of
Medicine, Cardiology Division, Tel Aviv 6423906, Israel
| | - Farhad Rikhtegar Nezami
- Institute for Medical Engineering and Science,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Elazer R. Edelman
- Institute for Medical Engineering and Science,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA
- Brigham and Women’s Hospital, Harvard Medical
School, Cardiovascular Division, Boston, MA 02115 USA
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14
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Chen Z, Pazdernik M, Zhang H, Wahle A, Guo Z, Bedanova H, Kautzner J, Melenovsky V, Kovarnik T, Sonka M. Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression. Med Image Anal 2018; 50:95-105. [PMID: 30253306 PMCID: PMC6237624 DOI: 10.1016/j.media.2018.09.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/26/2018] [Accepted: 09/05/2018] [Indexed: 01/25/2023]
Abstract
Cardiac allograft vasculopathy (CAV) accounts for about 30% of all heart-transplant (HTx) patient deaths. For patients at high risk for CAV complications after HTx, therapy must be initiated early to be effective. Therefore, new phenotyping approaches are needed to identify such HTx patients at the earliest possible time. Coronary optical coherence tomography (OCT) images were acquired from 50 HTx patients 1 and 12 months after HTx. Quantitative analysis of coronary wall morphology used LOGISMOS segmentation strategy to simultaneously identify three wall-layer surfaces for the entire pullback length in 3D: luminal, outer intimal, and outer medial surfaces. To quantify changes of coronary wall morphology between 1 and 12 months after HTx, the two pullbacks were mutually co-registered. Validation of layer thickness measurements showed high accuracy of performed layer analyses with layer thickness measures correlating well with manually-defined independent standard (Rautomated2 = 0.93, y=1.0x-6.2μm), average intimal+medial thickness errors were 4.98 ± 31.24 µm, comparable with inter-observer variability. Quantitative indices of coronary wall morphology 1 month and 12 months after HTx showed significant local as well as regional changes associated with CAV progression. Some of the newly available fully-3D baseline indices (intimal layer brightness, medial layer brightness, medial thickness, and intimal+medial thickness) were associated with CAV-related progression of intimal thickness showing promise of identifying patients subjected to rapid intimal thickening at 12 months after HTx from OCT-image data obtained just 1 month after HTx. Our approach allows quantification of location-specific alterations of coronary wall morphology over time and is sensitive even to very small changes of wall layer thicknesses that occur in patients following heart transplant.
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Affiliation(s)
- Zhi Chen
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
| | - Michal Pazdernik
- Institute of Clinical and Experimental Medicine (IKEM) in Prague, Czech Republic
| | - Honghai Zhang
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
| | - Andreas Wahle
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
| | - Zhihui Guo
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA
| | - Helena Bedanova
- Cardiovascular and Transplantation Surgery Center, Department of Cardiovascular Diseases, St. Annes University Hospital and Masaryk University Brno, Czech Republic
| | - Josef Kautzner
- Institute of Clinical and Experimental Medicine (IKEM) in Prague, Czech Republic
| | - Vojtech Melenovsky
- Institute of Clinical and Experimental Medicine (IKEM) in Prague, Czech Republic
| | - Tomas Kovarnik
- 2nd Department of Medicine - Department of Cardiovascular Medicine, First Faculty of Medicine, Charles University in Prague & General University Hospital in Prague, Czech Republic
| | - Milan Sonka
- Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA.
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15
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Gan M, Wang C, Yang T, Yang N, Zhang M, Yuan W, Li X, Wang L. Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights. BIOMEDICAL OPTICS EXPRESS 2018; 9:4481-4495. [PMID: 30615715 PMCID: PMC6157790 DOI: 10.1364/boe.9.004481] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/17/2018] [Accepted: 08/20/2018] [Indexed: 05/18/2023]
Abstract
Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.
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Affiliation(s)
- Meng Gan
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Cong Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163,
China
| | - Ting Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Na Yang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Miao Zhang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
| | - Wu Yuan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205,
USA
| | - Lirong Wang
- Department of Electronic and Information Engineering, Soochow University, Suzhou 215006,
China
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16
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Boi A, Jamthikar AD, Saba L, Gupta D, Sharma A, Loi B, Laird JR, Khanna NN, Suri JS. A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography. Curr Atheroscler Rep 2018; 20:33. [PMID: 29781047 DOI: 10.1007/s11883-018-0736-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques. RECENT FINDING Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque tissue characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.
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Affiliation(s)
- Alberto Boi
- Department of Cardiology, University of Cagliari, Cagliari, Italy
| | - Ankush D Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology Nagpur, Nagpur, Maharashtra, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology Nagpur, Nagpur, Maharashtra, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Bruno Loi
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | | | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Jasjit S Suri
- Coronary Arterial Division, AtheroPoint™, Roseville, CA, USA.
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