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Biccirè FG, Mannhart D, Kakizaki R, Windecker S, Räber L, Siontis GCM. Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review. Front Cardiovasc Med 2024; 11:1332925. [PMID: 38742173 PMCID: PMC11090039 DOI: 10.3389/fcvm.2024.1332925] [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: 11/03/2023] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
Background The diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far. Methods We performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools. Results Overall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91-0.99 for OCT and 0.89-0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82-0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82-0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70). Conclusion A limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information. Systematic Review Registration Open Science Framework (OSF), https://osf.io/nps2b/.Graphical AbstractCentral Illustration.
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Affiliation(s)
| | | | | | | | | | - George C. M. Siontis
- Department of Cardiology, Bern University Hospital, University of Bern, Bern, Switzerland
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Olender ML, Athanasiou LS, Michalis LK, Fotiadis DI, Edelman ER. A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2020; 14:1210-1220. [PMID: 33520048 PMCID: PMC7845913 DOI: 10.1109/jstsp.2020.3002385] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.
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Affiliation(s)
- Max L Olender
- Department of Mechanical Engineering and the Institute for Medical Engineering and Science, 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; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Lampros K Michalis
- Faculty of Medicine, School of Health Sciences, University of Ioannina and the 2nd Department of Cardiology, University Hospital of Ioannina, Ioannina, 45500 Greece
| | - Dimitris I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110 Greece; Department of Biomedical Research, Institute of Molecular Biology and Biotechnology - FORTH, Ioannina, 45110 Greece
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
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Abstract
Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.
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Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8091632] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.
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Hwang YN, Lee JH, Kim GY, Shin ES, Kim SM. Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:83-92. [PMID: 29157464 DOI: 10.1016/j.cmpb.2017.10.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 09/18/2017] [Accepted: 10/04/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. METHODS Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. RESULTS Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. CONCLUSIONS The proposed method had high clinical applicability for image-based tissue characterization.
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Affiliation(s)
- Yoo Na Hwang
- Department of Medical Biotechnology, Dongguk University-Bio Medi Campus (10326) 32, Dongguk-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Ju Hwan Lee
- Department of Medical Devices Industry, Dongguk University-Seoul (04620) 26, Pil-dong 3-ga, Jung-gu, Seoul, Republic of Korea
| | - Ga Young Kim
- Department of Medical Biotechnology, Dongguk University-Bio Medi Campus (10326) 32, Dongguk-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Eun Seok Shin
- Department of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine (44033) 877, Bangeojinsunhwando-ro, Dong-gu, Ulsan, Republic of Korea
| | - Sung Min Kim
- Department of Medical Biotechnology, Dongguk University-Bio Medi Campus (10326) 32, Dongguk-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Medical Devices Industry, Dongguk University-Seoul (04620) 26, Pil-dong 3-ga, Jung-gu, Seoul, Republic of Korea.
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Athanasiou LS, Rigas GA, Sakellarios AI, Exarchos TP, Siogkas PK, Naka KK, Panetta D, Pelosi G, Vozzi F, Michalis LK, Parodi O, Fotiadis DI. Computerized methodology for micro-CT and histological data inflation using an IVUS based translation map. Comput Biol Med 2015; 65:168-76. [PMID: 25771781 DOI: 10.1016/j.compbiomed.2015.02.018] [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: 11/15/2014] [Revised: 02/25/2015] [Accepted: 02/26/2015] [Indexed: 10/23/2022]
Abstract
A framework for the inflation of micro-CT and histology data using intravascular ultrasound (IVUS) images, is presented. The proposed methodology consists of three steps. In the first step the micro-CT/histological images are manually co-registered with IVUS by experts using fiducial points as landmarks. In the second step the lumen of both the micro-CT/histological images and IVUS images are automatically segmented. Finally, in the third step the micro-CT/histological images are inflated by applying a transformation method on each image. The transformation method is based on the IVUS and micro-CT/histological contour difference. In order to validate the proposed image inflation methodology, plaque areas in the inflated micro-CT and histological images are compared with the ones in the IVUS images. The proposed methodology for inflating micro-CT/histological images increases the sensitivity of plaque area matching between the inflated and the IVUS images (7% and 22% in histological and micro-CT images, respectively).
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Affiliation(s)
- Lambros S Athanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece
| | - George A Rigas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece
| | - Antonis I Sakellarios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece
| | - Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece
| | - Panagiotis K Siogkas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece
| | - Katerina K Naka
- Michaelidion Cardiac Center and Dept. of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece
| | - Daniele Panetta
- Institute of Clinical Physiology, National Research Council, Pisa, IT, 56124, Italy
| | - Gualtiero Pelosi
- Institute of Clinical Physiology, National Research Council, Pisa, IT, 56124, Italy
| | - Federico Vozzi
- Institute of Clinical Physiology, National Research Council, Pisa, IT, 56124, Italy
| | - Lampros K Michalis
- Michaelidion Cardiac Center and Dept. of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece
| | - Oberdan Parodi
- Institute of Clinical Physiology, National Research Council, Pisa, IT, 56124, Italy
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece; Michaelidion Cardiac Center and Dept. of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece.
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Athanasiou L, Sakellarios AI, Bourantas CV, Tsirka G, Siogkas P, Exarchos TP, Naka KK, Michalis LK, Fotiadis DI. Currently available methodologies for the processing of intravascular ultrasound and optical coherence tomography images. Expert Rev Cardiovasc Ther 2015; 12:885-900. [PMID: 24949801 DOI: 10.1586/14779072.2014.922413] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Optical coherence tomography and intravascular ultrasound are the most widely used methodologies in clinical practice as they provide high resolution cross-sectional images that allow comprehensive visualization of the lumen and plaque morphology. Several methods have been developed in recent years to process the output of these imaging modalities, which allow fast, reliable and reproducible detection of the luminal borders and characterization of plaque composition. These methods have proven useful in the study of the atherosclerotic process as they have facilitated analysis of a vast amount of data. This review presents currently available intravascular ultrasound and optical coherence tomography processing methodologies for segmenting and characterizing the plaque area, highlighting their advantages and disadvantages, and discusses the future trends in intravascular imaging.
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Affiliation(s)
- Lambros Athanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
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