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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS. Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos. Comput Biol Med 2017; 84:168-181. [DOI: 10.1016/j.compbiomed.2017.03.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 03/12/2017] [Accepted: 03/27/2017] [Indexed: 01/22/2023]
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Lee W, Roh Y. Ultrasonic transducers for medical diagnostic imaging. Biomed Eng Lett 2017; 7:91-97. [PMID: 30603155 PMCID: PMC6208471 DOI: 10.1007/s13534-017-0021-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 02/07/2017] [Accepted: 02/23/2017] [Indexed: 10/20/2022] Open
Abstract
Over the past decades, ultrasound imaging technology has made tremendous progress in obtaining important diagnostic information from patients in a rapid, noninvasive manner. Although the technology has benefited from sophisticated signal processing technology and imaging system integration, much of this progress has been derived from the development of ultrasonic transducers that are in direct contact with patients. An overview of medical ultrasonic imaging transducers is presented in this review that describes their structure, types, and application fields. The structural components of a typical transducer are presented in detail including an active layer, acoustic matching layers, a backing block, an acoustic lens, and kerfs. The types of transducers are classified according to the dimensions of ultrasound images: one-dimensional array, mechanical wobbling, and two-dimensional array transducers. Advantages of each transducer over the other and the technical issues for further performance enhancement are described. Application of the transducers to various clinical imaging fields is also reviewed.
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Affiliation(s)
- Wonseok Lee
- Probe Development Team, Alpinion Medical Systems Co., Ltd., Seoul, Korea
| | - Yongrae Roh
- School of Mechanical Engineering, Kyungpook National University, Daegu, Korea
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Talou GDM, Blanco PJ, Larrabide I, Bezerra CG, Lemos PA, Feijoo RA. Registration Methods for IVUS: Transversal and Longitudinal Transducer Motion Compensation. IEEE Trans Biomed Eng 2017; 64:890-903. [DOI: 10.1109/tbme.2016.2581583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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54
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Su S, Hu Z, Lin Q, Hau WK, Gao Z, Zhang H. An artificial neural network method for lumen and media-adventitia border detection in IVUS. Comput Med Imaging Graph 2017; 57:29-39. [DOI: 10.1016/j.compmedimag.2016.11.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 11/04/2016] [Accepted: 11/08/2016] [Indexed: 10/20/2022]
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Coron A, Mamou J, Saegusa-Beecroft E, Yamaguchi T, Yanagihara E, Machi J, Bridal SL, Feleppa EJ. Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes. IEEE Trans Biomed Eng 2016; 64:1579-1591. [PMID: 28113305 DOI: 10.1109/tbme.2016.2614137] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To detect metastases in freshly excised human lymph nodes (LNs) using three-dimensional (3-D), high-frequency, quantitative ultrasound (QUS) methods, the LN parenchyma (LNP) must be segmented to preclude QUS analysis of data in regions outside the LNP and to compensate ultrasound attenuation effects due to overlying layers of LNP and residual perinodal fat (PNF). METHODS After restoring the saturated radio-frequency signals from PNF using an approach based on smoothing cubic splines, the three regions, i.e., LNP, PNF, and normal saline (NS), in the LN envelope data are segmented using a new, automatic, 3-D, three-phase, statistical transverseslice-based level-set (STS-LS) method that amends Lankton's method. Due to ultrasound attenuation and focusing effects, the speckle statistics of the envelope data vary with imaged depth. Thus, to mitigate depth-related inhomogeneity effects, the STS-LS method employs gamma probabilitydensity functions to locally model the speckle statistics within consecutive transverse slices. RESULTS Accurate results were obtained on simulated data. On a representative dataset of 54 LNs acquired from colorectal-cancer patients, the Dice similarity coefficient for LNP, PNF, and NS were 0.938 ± 0.025, 0.832 ± 0.086, and 0.968 ± 0.008, respectively, when compared to expert manual segmentation. CONCLUSION The STS-LS outperforms the established methods based on global and local statistics in our datasets and is capable of accurately handling the depth-dependent effects due to attenuation and focusing. SIGNIFICANCE This advance permits the automatic QUS-based cancer detection in the LNs. Furthermore, the STS-LS method could potentially be used in a wide range of ultrasound-imaging applications suffering from depth-dependent effects.
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Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization. Med Image Anal 2016; 32:1-17. [DOI: 10.1016/j.media.2016.02.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Revised: 11/20/2015] [Accepted: 02/18/2016] [Indexed: 11/18/2022]
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57
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A physics-based intravascular ultrasound image reconstruction method for lumen segmentation. Comput Biol Med 2016; 75:19-29. [DOI: 10.1016/j.compbiomed.2016.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 05/02/2016] [Accepted: 05/14/2016] [Indexed: 11/21/2022]
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Sun Z, Bai H, Liu B. Rigid and elastic registration for coronary artery IVUS images. Technol Health Care 2016; 24 Suppl 2:S455-63. [DOI: 10.3233/thc-161168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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59
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Araki T, Ikeda N, Shukla D, Jain PK, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 128:137-158. [PMID: 27040838 DOI: 10.1016/j.cmpb.2016.02.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 02/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Pankaj K Jain
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-Of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA.
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Araki T, Ikeda N, Shukla D, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:161-179. [PMID: 26707374 DOI: 10.1016/j.cmpb.2015.10.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/12/2015] [Accepted: 10/21/2015] [Indexed: 06/05/2023]
Abstract
Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) - a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | | | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Lo Vercio L, Orlando JI, Del Fresno M, Larrabide I. Assessment of image features for vessel wall segmentation in intravascular ultrasound images. Int J Comput Assist Radiol Surg 2016; 11:1397-407. [PMID: 26811082 DOI: 10.1007/s11548-015-1345-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 12/24/2015] [Indexed: 11/25/2022]
Abstract
BACKGROUND Intravascular ultrasound (IVUS) provides axial greyscale images, allowing the assessment of the vessel wall and the surrounding tissues. Several studies have described automatic segmentation of the luminal boundary and the media-adventitia interface by means of different image features. PURPOSE The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image features for segmenting IVUS images. The study is focused on Volcano 20 MHz frames not containing plaque or containing fibrotic plaques, and, in principle, it could not be applied to frames containing shadows, calcified plaques, bifurcations and side vessels. METHODS Several image filters, textural descriptors, edge detectors, noise and spatial measures were taken into account. The assessment is based on classification techniques previously used for IVUS segmentation, assigning to each pixel a continuous likelihood value obtained using support vector machines (SVMs). To retrieve relevant features, sequential feature selection was performed guided by the area under the precision-recall curve (AUC-PR). RESULTS Subsets of relevant image features for lumen, plaque and surrounding tissues characterization were obtained, and SVMs trained with these features were able to accurately identify those regions. The experimental results were evaluated with respect to ground truth segmentations from a publicly available dataset, reaching values of AUC-PR up to 0.97 and Jaccard index close to 0.85. CONCLUSION Noise-reduction filters and Haralick's textural features denoted their relevance to identify lumen and background. Laws' textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process.
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Affiliation(s)
- Lucas Lo Vercio
- Pladema, UNICEN, Tandil, Argentina.
- CONICET, Tandil, Argentina.
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016; 9:234-63. [PMID: 26742143 PMCID: PMC5233461 DOI: 10.1109/rbme.2016.2515127] [Citation(s) in RCA: 212] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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63
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Araki T, Banchhor SK, Londhe ND, Ikeda N, Radeva P, Shukla D, Saba L, Balestrieri A, Nicolaides A, Shafique S, Laird JR, Suri JS. Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos. J Med Syst 2015; 40:51. [PMID: 26643081 DOI: 10.1007/s10916-015-0407-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 11/16/2015] [Indexed: 11/29/2022]
Abstract
Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm(3), 27.79 ± 10.94 mm(3), 46.44 ± 19.13 mm(3) and 35.92 ± 16.44 mm(3) respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student's t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80%. Out procedure and protocol is along the line with method previously published clinically.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Petia Radeva
- Department MAIA, Computer Vision Centre, Cerdanyola del Vallés, University of Barcelona, Barcelona, Spain
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK.,Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. .,Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA. .,Department of Electrical Engineering, University of Idaho (Affl.), Moscow, ID, USA.
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Gao Z, Hau WK, Lu M, Huang W, Zhang H, Wu W, Liu X, Zhang YT. Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:2001-2021. [PMID: 25922134 DOI: 10.1016/j.ultrasmedbio.2015.03.022] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 03/16/2015] [Accepted: 03/19/2015] [Indexed: 06/04/2023]
Abstract
An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors (lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease.
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Affiliation(s)
- Zhifan Gao
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China
| | - William Kongto Hau
- Institute of Cardiovascular Medicine and Research, LiKaShing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Minhua Lu
- Department of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China
| | - Wenhua Huang
- Institute of Clinical Anatomy, Southern Medical University, Guangzhou, China
| | - Heye Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China.
| | - Wanqing Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Liu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China
| | - Yuan-Ting Zhang
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China; The Joint Research Centre for Biomedical Engineering, Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, China
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65
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Zhao F, Xie X, Roach M. Computer Vision Techniques for Transcatheter Intervention. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2015; 3:1900331. [PMID: 27170893 PMCID: PMC4848047 DOI: 10.1109/jtehm.2015.2446988] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 04/10/2015] [Accepted: 06/09/2015] [Indexed: 12/02/2022]
Abstract
Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.
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Affiliation(s)
- Feng Zhao
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
| | - Xianghua Xie
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
| | - Matthew Roach
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
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Gorpas D, Fatakdawala H, Bec J, Ma D, Yankelevich DR, Qi J, Marcu L. Fluorescence lifetime imaging and intravascular ultrasound: co-registration study using ex vivo human coronaries. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:156-66. [PMID: 25163056 PMCID: PMC4428614 DOI: 10.1109/tmi.2014.2350491] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Fluorescence lifetime imaging (FLIM) has demonstrated potential for robust assessment of atherosclerotic plaques biochemical composition and for complementing conventional intravascular ultrasound (IVUS), which provides information on plaque morphology. The success of such a bi-modal imaging modality depends on accurate segmentation of the IVUS images and proper angular registration between these two modalities. This paper reports a novel IVUS segmentation methodology addressing this issue. The image preprocessing consisted of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries was achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensured boundary smoothness and continuity. The detection of the guide-wire artifact in both modalities is used for angular registration. Intraluminal studies were conducted in 13 ex vivo segments of human coronaries. The IVUS segmentation accuracy was assessed against independent manual tracings, providing 91.82% sensitivity and 97.55% specificity. The proposed methodology makes the bi-modal FLIM and IVUS approach feasible for comprehensive intravascular diagnosis by providing co-registered biochemical and morphological information of atherosclerotic plaques.
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Affiliation(s)
- Dimitris Gorpas
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Hussain Fatakdawala
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Julien Bec
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Dinglong Ma
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Diego R. Yankelevich
- Department of Biomedical Engineering and the Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, CA 95616 USA
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Jones JL, Xie X, Essa E. Combining region-based and imprecise boundary-based cues for interactive medical image segmentation. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2014; 30:1649-1666. [PMID: 25377853 DOI: 10.1002/cnm.2693] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 08/27/2014] [Accepted: 10/21/2014] [Indexed: 06/04/2023]
Abstract
In this paper, we present an approach combining both region selection and user point selection for user-assisted segmentation as either an enclosed object or an open curve, investigate the method of image segmentation in specific medical applications (user-assisted segmentation of the media-adventitia border in intravascular ultrasound images, and lumen border in optical coherence tomography images), and then demonstrate the method with generic images to show how it could be utilized in other types of medical image and is not limited to the applications described. The proposed method combines point-based soft constraint on object boundary and stroke-based regional constraint. The user points act as attraction points and are treated as soft constraints rather than hard constraints that the segmented boundary has to pass through. The user can also use strokes to specify region of interest. The probabilities of region of interest for each pixel are then calculated, and their discontinuity is used to indicate object boundary. The combinations of different types of user constraints and image features allow flexible and robust segmentation, which is formulated as an energy minimization problem on a multilayered graph and is solved using a shortest path search algorithm. We show that this combinatorial approach allows efficient and effective interactive segmentation, which can be used with both open and closed curves to segment a variety of images in different ways. The proposed method is demonstrated in the two medical applications, that is, intravascular ultrasound and optical coherence tomography images, where image artefacts such as acoustic shadow and calcification are commonplace and thus user guidance is desirable. We carried out both qualitative and quantitative analysis of the results for the medical data; comparing the proposed method against a number of interactive segmentation techniques.
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Coronary atheroma composition and its association with segmental endothelial dysfunction in non-ST segment elevation myocardial infarction: novel insights with radiofrequency (iMAP) intravascular ultrasonography. Int J Cardiovasc Imaging 2014; 31:247-57. [DOI: 10.1007/s10554-014-0545-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 09/29/2014] [Indexed: 01/23/2023]
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Computer methods for follow-up study of hemodynamic and disease progression in the stented coronary artery by fusing IVUS and X-ray angiography. Med Biol Eng Comput 2014; 52:539-56. [DOI: 10.1007/s11517-014-1155-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 04/02/2014] [Indexed: 10/25/2022]
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Segmentation method of intravascular ultrasound images of human coronary arteries. Comput Med Imaging Graph 2014; 38:91-103. [DOI: 10.1016/j.compmedimag.2013.09.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Revised: 09/06/2013] [Accepted: 09/10/2013] [Indexed: 11/22/2022]
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Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 2013; 38:70-90. [PMID: 24012215 DOI: 10.1016/j.compmedimag.2013.07.001] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Revised: 03/15/2013] [Accepted: 07/01/2013] [Indexed: 11/21/2022]
Abstract
This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.
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An IVUS image-based approach for improvement of coronary plaque characterization. Comput Biol Med 2013; 43:268-80. [PMID: 23410676 DOI: 10.1016/j.compbiomed.2012.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2011] [Revised: 12/09/2012] [Accepted: 12/27/2012] [Indexed: 11/21/2022]
Abstract
Virtual Histology-Intravascular Ultrasound (VH-IVUS) is widely used for studying atherosclerosis plaque composition. However, one of the main limitations of the VH-IVUS relates to its dependence to the Electrocardiogram (ECG)-gated acquisition. To overcome this limitation, this paper proposes a robust image-based approach for characterization of the plaques using IVUS images. The proposed method consists of three main steps of (1) shadow detection: as an efficient preprocessing step to identify and remove acoustic shadow regions; (2) feature extraction: a combination of gray-scale based features and textural descriptors; and (3) classification: to classify each pixel into one of the three classes (calcium, necrotic core and fibro-fatty). In order to evaluate the efficiency of the proposed algorithm two in-vivo and ex-vivo data sets are considered. The kappa values of 0.639 on in-vivo and 0.628 on ex-vivo tests with VH-IVUS and the histology images labeled by the experts respectively indicate the effectiveness of the proposed algorithm.
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Hartley CJ, Naghavi M, Parodi O, Pattichis CS, Poon CCY, Zhang YT. Cardiovascular health informatics: risk screening and intervention. ACTA ACUST UNITED AC 2013; 16:791-4. [PMID: 22997187 DOI: 10.1109/titb.2012.2216057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.
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