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Jeong GJ, Lee G, Lee JG, Kang SJ. Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease. Korean Circ J 2024; 54:30-39. [PMID: 38111183 PMCID: PMC10784613 DOI: 10.4070/kcj.2023.0166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/21/2023] [Accepted: 09/19/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. METHODS A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. RESULTS At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. CONCLUSIONS The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.
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Affiliation(s)
- Gyu-Jun Jeong
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea
| | - Gaeun Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea.
| | - Soo-Jin Kang
- Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Huang X, Bajaj R, Li Y, Ye X, Lin J, Pugliese F, Ramasamy A, Gu Y, Wang Y, Torii R, Dijkstra J, Zhou H, Bourantas CV, Zhang Q. POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation. Med Image Anal 2023; 89:102922. [PMID: 37598605 DOI: 10.1016/j.media.2023.102922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 07/06/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.
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Affiliation(s)
- Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK; School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang, China
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yilong Li
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Xin Ye
- Zhejiang Provincial People's Hospital, 270 West Xueyuan Road, Wenzhou, Zhejiang, China
| | - Ji Lin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yue Gu
- Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou, China
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | | | - Huiyu Zhou
- School of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK.
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Collins GC, Rojas SS, Bercu ZL, Desai JP, Lindsey BD. Supervised segmentation for guiding peripheral revascularization with forward-viewing, robotically steered ultrasound guidewire. Med Phys 2023; 50:3459-3474. [PMID: 36906877 PMCID: PMC10272103 DOI: 10.1002/mp.16350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 01/19/2023] [Accepted: 02/26/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients. PURPOSE Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically-steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement. METHODS The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally-acquired data with a forward-viewing, robotically-steered guidewire imaging system. B-mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U-net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially-occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D-printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison. RESULTS An aperture size of 3.8 mm resulted in the best-performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U-net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best-performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing in ex vivo arteries, average binary accuracy, F1 score, Jaccard index, and sensitivity each exceeded 0.9. CONCLUSIONS Segmentation of ultrasound images of partially-occluded peripheral arteries acquired with a forward-viewing, robotically-steered guidewire system was demonstrated for the first-time using representation learning. This could represent a fast, accurate approach for guiding peripheral revascularization.
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Affiliation(s)
- Graham C. Collins
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
| | - Stephan Strassle Rojas
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 30309
| | - Zachary L. Bercu
- Interventional Radiology, Emory University School of Medicine, Atlanta, GA, USA, 30308
| | - Jaydev P. Desai
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
| | - Brooks D. Lindsey
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 30309
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Arora P, Singh P, Girdhar A, Vijayvergiya R. A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images. Cardiovasc Eng Technol 2023; 14:264-295. [PMID: 36650320 DOI: 10.1007/s13239-023-00654-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 11/28/2022] [Accepted: 01/02/2023] [Indexed: 01/19/2023]
Abstract
Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.
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Affiliation(s)
- Priyanka Arora
- Research Scholar, IKG Punjab Technical University, Punjab, India. .,Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
| | - Parminder Singh
- Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Akshay Girdhar
- Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Rajesh Vijayvergiya
- Department of Cardiology, Advanced Cardiac Centre, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
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Du H, Ling L, Yu W, Wu P, Yang Y, Chu M, Yang J, Yang W, Tu S. Convolutional networks for the segmentation of intravascular ultrasound images: Evaluation on a multicenter dataset. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106599. [PMID: 34974233 DOI: 10.1016/j.cmpb.2021.106599] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/21/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The delineation of the lumen contour and external elastic lamina (EEL) in intravascular ultrasound (IVUS) images is crucial for the quantitative analysis of coronary atherosclerotic plaques. However, the presence of ultrasonic shadows and anatomical structures (such as bifurcations and calcified plaque) complicates the automatic delineation of the lumen contour and EEL. The purpose of this paper is to evaluate the IVUS segmentation performances of different convolutional networks and the impact factors on a large-scale multiple-center dataset. METHODS A total of 6516 cross-sectional images from 175 IVUS pullbacks acquired in different centers by different IVUS imaging catheters were screened from a corelab to evaluate the segmentation methods. The IVUS images included bifurcation, side branch ostia, and various image artifacts to reflect the general image characteristics in routine clinical acquisition. We compared three generic fully convolutional networks (FCNs) and two FCNs specifically designed for the segmentation of IVUS images and explored the factors impacting the segmentation performance, including the training images and the input of consecutive images to the models. The performance of the FCNs was evaluated by using the Dice similarity coefficient (DSC), the Jaccard index (JI), the Hausdorff distance (HD), linear regression and Bland-Altman analysis. RESULTS The 4-cascaded RefineNet and DeepLabv3+ outperformed U-net and IVUS-net in the segmentation of the lumen contour and EEL on IVUS images. DeepLabv3+ had the best segmentation performance, with DSCs of 0.927 and 0.944, JIs of 0.911 and 0.933, and HDs of 0.336 mm and 0.367 mm for delineation of the lumen and EEL, respectively. Excellent agreement between DeepLabv3+ and the manual delineation was found in the quantification of the coronary plaque area (r = 0.98). CONCLUSIONS The convolutional network architecture is effective in the automatic segmentation of IVUS images. It might contribute to the clinical application of quantitative IVUS analysis in real-world as well as the efficient assessment of coronary atherosclerosis.
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Affiliation(s)
- Haiyan Du
- School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai, South Road Baiyun District, Guangzhou, Guangdong 510515, China
| | - Li Ling
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road Xuhui District, Shanghai 200030, China
| | - Wei Yu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road Xuhui District, Shanghai 200030, China
| | - Peng Wu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road Xuhui District, Shanghai 200030, China
| | - Yuan Yang
- School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai, South Road Baiyun District, Guangzhou, Guangdong 510515, China
| | - Miao Chu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road Xuhui District, Shanghai 200030, China
| | - Junqing Yang
- Department of Cardiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai, South Road Baiyun District, Guangzhou, Guangdong 510515, China.
| | - Shengxian Tu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road Xuhui District, Shanghai 200030, China.
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Huang Y, Xia M, Guo Y, Zhou G, Wang Y. Extraction of media adventitia and luminal intima borders by reconstructing intravascular ultrasound image sequences with vascular structural continuity. Med Phys 2021; 48:4350-4364. [PMID: 34101854 DOI: 10.1002/mp.15037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 05/06/2021] [Accepted: 05/29/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Most published methods directly achieve vessel membrane border detection on cross-sectional intravascular ultrasound (IVUS) images. The vascular structural continuity that exists in entire IVUS image sequences has been overlooked. However, this continuity can have a helpful role in the delineation of vessel membrane contours. To achieve the vessel membrane segmentation more effectively through employing this continuity, a strategy, referred to as multiangle reconstruction, segmentation, and recovery (RSR), is proposed in this paper. METHODS Four main steps are contained in the multiangle-RSR: first, a combination of sampling and interpolation is employed to reconstruct long-axis-model IVUS frames, in which continuity information becomes available. Second, a clustering algorithm is conducted on long-axis-model IVUS frames to roughly extract the media-adventitia (MA) and lumen-intima (LI) boundaries. Third, the segmentation results of cross-sectional IVUS frames are recovered based on the rough results of long-axis-model IVUS frames, and an optimization process that combines downsampling, fitting and smoothing is designed to reduce the interference of bifurcation and side vessels. RESULTS Multiangle-RSR is tested on a public dataset, and the Hausdorff distance (HD), Jaccard measure (JM), and percentage of area difference (PAD) are utilized as quantitative evaluation metrics. Mean HDs of 0.34 and 0.29 mm are obtained for MA border detection and LI border detection, respectively, which decrease by 43.3% and 9.4%, respectively, compared with their counterparts in previously published approaches. Furthermore, the mean JM is 0.87 for both MA border detection and LI border detection. The mean PADs of the MA contour extraction and the LI contour extraction are 0.10 and 0.11, respectively. CONCLUSION The results indicate that the proposed strategy effectively introduces vascular structural continuity by reconstructing long-axis-model IVUS frames and achieves more precise extraction of MA and LI borders.
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Affiliation(s)
- Yi Huang
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Menghua Xia
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Yi Guo
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Guohui Zhou
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- Department of Electrical Engineering, Fudan University, Shanghai, China
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Huang Y, Yan W, Xia M, Guo Y, Zhou G, Wang Y. Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector and an effective selection strategy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105339. [PMID: 31978806 DOI: 10.1016/j.cmpb.2020.105339] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/03/2020] [Accepted: 01/14/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Segmenting vessel membranes and locating the calcific region in intravascular ultrasound (IVUS) images aid physicians in the diagnosis of atherosclerosis. However, the manual extraction of the media adventitia (MA)/lumen border and calcification location are cumbersome due to the excessive number of IVUS frames. Moreover, most existing (semi-)automatic detection methods cannot achieve both vessel membrane extraction and calcification location simultaneously, and they are unable to detect vessel membranes in IVUS frames from different acquisition systems. METHOD A fully automatic approach is proposed based on extremal regions and a flexible selection strategy to extract vessel membranes in different IVUS frames and locate the calcific region in high-frequency ones. Three main steps are included in the algorithm. First, a region detector is employed to extract extremal regions from an IVUS image. Then, according to the selection strategy, a part of the extracted regions is selected. At the same time, the calcification is located according to its special acoustic properties. Next, approximate MA and lumen border segmentation is achieved based on the selected extremal regions and the located calcification in polar coordinates. Finally, the final segmentation results are obtained by smoothing the approximate values. RESULT To demonstrate the feasibility of the method, it was evaluated based on a standard public dataset. Furthermore, to quantitatively evaluate the segmentation performance, the Hausdorff distance (HD), Jaccard measure (JM) and percentage of area difference (PAD) were used. The results show that a mean HD of 1.13/1.21 mm, a mean JM of 0.83/0.77 and a mean PAD of 0.11/0.23 are achieved for MA/lumen border detection in 77 40-MHz IVUS images. For MA/lumen border extraction in 435 20-MHz IVUS frames, the average HD, JM and PAD values are 0.47/0.28 mm, 0.84/0.89 and 0.13/0.10, respectively. In addition, the approach successfully achieves calcification location in 40-MHz IVUS frames. In comparison with other published methods, the method proposed in this study is competitive. CONCLUSION According to these results, our strategy can extract MA/lumen borders in different IVUS frames and effectively locate calcification in high-frequency IVUS frames.
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Affiliation(s)
- Yi Huang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Wenjun Yan
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Menghua Xia
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China
| | - Yi Guo
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, 200433, China
| | - Guohui Zhou
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, 200433, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, 200433, China.
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Lo Vercio L, Del Fresno M, Larrabide I. Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:113-121. [PMID: 31319939 DOI: 10.1016/j.cmpb.2019.05.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/26/2019] [Accepted: 05/20/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. METHODS Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. RESULTS The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30. CONCLUSIONS A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.
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Affiliation(s)
- Lucas Lo Vercio
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
| | - Mariana Del Fresno
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Comisión de Investigaciones Científicas de la Provincia deBuenos Aires (CICPBA), Argentina
| | - Ignacio Larrabide
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
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IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution. Comput Biol Med 2019; 109:207-217. [DOI: 10.1016/j.compbiomed.2019.04.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 04/22/2019] [Accepted: 04/22/2019] [Indexed: 11/22/2022]
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Hammouche A, Cloutier G, Tardif JC, Hammouche K, Meunier J. Automatic IVUS lumen segmentation using a 3D adaptive helix model. Comput Biol Med 2019; 107:58-72. [DOI: 10.1016/j.compbiomed.2019.01.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 01/23/2019] [Accepted: 01/24/2019] [Indexed: 10/27/2022]
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Wang YY, Qiu CH, Jiang J, Xia SR. Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach. ULTRASONIC IMAGING 2019; 41:78-93. [PMID: 30556484 DOI: 10.1177/0161734618820112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.
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Affiliation(s)
- Yuan-Yuan Wang
- School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Chen-Hui Qiu
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Jun Jiang
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shun-Ren Xia
- School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
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Wang YY, Peng WX, Qiu CH, Jiang J, Xia SR. Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images. ULTRASONICS 2019; 92:1-7. [PMID: 30205179 DOI: 10.1016/j.ultras.2018.06.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 05/25/2018] [Accepted: 06/16/2018] [Indexed: 06/08/2023]
Abstract
Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.
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Affiliation(s)
- Yuan-Yuan Wang
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Wen-Xian Peng
- Radiology Department of Hangzhou Medical College, China
| | - Chen-Hui Qiu
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Jun Jiang
- Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Shun-Ren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China.
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13
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Kermani A, Ayatollahi A. A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames. Comput Biol Med 2018; 104:10-28. [PMID: 30419417 DOI: 10.1016/j.compbiomed.2018.10.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/20/2018] [Accepted: 10/23/2018] [Indexed: 11/18/2022]
Abstract
Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.
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Affiliation(s)
- Ali Kermani
- School of Electrical Engineering, Iran University of Science and Technology, Iran
| | - Ahmad Ayatollahi
- School of Electrical Engineering, Iran University of Science and Technology, Iran.
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Faraji M, Cheng I, Naudin I, Basu A. Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection. ULTRASONICS 2018; 84:356-365. [PMID: 29241056 DOI: 10.1016/j.ultras.2017.11.020] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 06/07/2023]
Abstract
Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and media-adventitia borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information. We extensively evaluated our selection strategy on the test set of a standard publicly available dataset containing 326 IVUS B-mode images. We showed that in the best case, the average Hausdorff Distances (HD) between the extracted ERELs and the actual lumen and media were 0.22 mm and 0.45 mm, respectively. The results of our experiments revealed that our selection strategy was able to segment the lumen with ⩽0.3 mm HD to the gold standard even though the images contained major artifacts such as bifurcations, shadows, and side branches. Moreover, when there was no artifact, our proposed method was able to delineate media-adventitia boundaries with 0.31 mm HD to the gold standard. Furthermore, our proposed segmentation method runs in time that is linear in the number of pixels in each frame. Based on the results of this work, by using a 20 MHz IVUS probe with controlled pullback, not only can we now analyze the internal structure of human arteries more accurately, but also segment each frame during the pullback procedure because of the low run time of our proposed segmentation method.
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Affiliation(s)
- Mehdi Faraji
- Department of Computing Science, University of Alberta, Canada.
| | - Irene Cheng
- Department of Computing Science, University of Alberta, Canada.
| | | | - Anup Basu
- Department of Computing Science, University of Alberta, Canada.
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Ilunga-Mbuyamba E, Avina-Cervantes JG, Lindner D, Arlt F, Ituna-Yudonago JF, Chalopin C. Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images. Int J Comput Assist Radiol Surg 2018; 13:331-342. [PMID: 29330658 DOI: 10.1007/s11548-018-1703-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 01/04/2018] [Indexed: 11/27/2022]
Abstract
PURPOSE Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. METHODS A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. RESULTS Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. CONCLUSION The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.
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Affiliation(s)
- Elisee Ilunga-Mbuyamba
- CA Telematics, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, 36885, Salamanca, Mexico
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103, Leipzig, Germany
| | - Juan Gabriel Avina-Cervantes
- CA Telematics, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, 36885, Salamanca, Mexico.
| | - Dirk Lindner
- Department of Neurosurgery, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Felix Arlt
- Department of Neurosurgery, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Jean Fulbert Ituna-Yudonago
- CA Telematics, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, 36885, Salamanca, Mexico
| | - Claire Chalopin
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103, Leipzig, Germany
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Zakeri FS, Setarehdan SK, Norouzi S. Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model. Comput Biol Med 2017; 89:561-572. [DOI: 10.1016/j.compbiomed.2017.03.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 03/22/2017] [Accepted: 03/23/2017] [Indexed: 10/19/2022]
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17
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Zahnd G, Hoogendoorn A, Combaret N, Karanasos A, Péry E, Sarry L, Motreff P, Niessen W, Regar E, van Soest G, Gijsen F, van Walsum T. Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions. Int J Comput Assist Radiol Surg 2017; 12:1923-1936. [PMID: 28801817 PMCID: PMC5656722 DOI: 10.1007/s11548-017-1657-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 08/03/2017] [Indexed: 11/29/2022]
Abstract
PURPOSE Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. METHODS First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. RESULTS The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98). CONCLUSION The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.
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Affiliation(s)
- Guillaume Zahnd
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
| | - Ayla Hoogendoorn
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Nicolas Combaret
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France.,Department of Cardiology, Gabriel-Montpied Hospital, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Antonios Karanasos
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Emilie Péry
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France
| | - Laurent Sarry
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France
| | - Pascal Motreff
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France.,Department of Cardiology, Gabriel-Montpied Hospital, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Evelyn Regar
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Gijs van Soest
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Frank Gijsen
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
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Jodas DS, Pereira AS, Tavares JMRS. Automatic segmentation of the lumen region in intravascular images of the coronary artery. Med Image Anal 2017. [PMID: 28624754 DOI: 10.1016/j.media.2017.06.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.
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Affiliation(s)
- Danilo Samuel Jodas
- CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| | - Aledir Silveira Pereira
- Universidade Estadual Paulista "Júlio de Mesquita Filho", Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal. http://www.fe.up.pt/~tavares
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Assessment of Inter-Expert Variability and of an Automated Segmentation Method of 40 and 60 MHz IVUS Images of Coronary Arteries. PLoS One 2017; 12:e0168332. [PMID: 28107355 PMCID: PMC5249211 DOI: 10.1371/journal.pone.0168332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 11/11/2016] [Indexed: 11/25/2022] Open
Abstract
The objectives were to compare the performance of a segmentation algorithm, based on the minimization of an uncertainty function, to delineate contours of external elastic membrane and lumen of human coronary arteries imaged with 40 and 60 MHz IVUS, and to use values of this function to delineate portions of contours with highest uncertainty. For 8 patients, 40 and 60 MHz IVUS coronary data acquired pre- and post-interventions were used, for a total of 68,516 images. Manual segmentations of contours (on 2312 images) performed by experts at three core laboratories were the gold-standards. Inter-expert variability was highest on contour points with largest values of the uncertainty function (p < 0.001). Inter-expert variability was lower at 60 than 40 MHz for external elastic membrane (p = 0.013) and lumen (p = 0.024). Average differences in plaque (and atheroma) burden between algorithmic contours and experts’ contours were within inter-expert variability (p < 0.001).
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20
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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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21
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Singh V, Elamvazuthi I, Jeoti V, George J, Swain A, Kumar D. Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images. Biomed Eng Online 2016; 15:13. [PMID: 26838596 PMCID: PMC4736278 DOI: 10.1186/s12938-016-0129-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 01/21/2016] [Indexed: 12/03/2022] Open
Abstract
Background Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation. Methods The developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimized by the particle swarm optimization algorithm. Thereafter, the optimized image is processed by the Chan–Vese method to extract the ATFL region through curve evolution; then the resultant image smoothed by morphological operation. The algorithm is tested on 25 subjects’ datasets and the corresponding performance metrics are evaluated to demonstrate its clinical applicability. Results The performance of the developed framework is evaluated based on various measurement metrics. It was found that estimated computational performance of the developed framework is 12 times faster than existing Chan–Vese method. Furthermore, the developed framework yielded the average sensitivity of 98.3 %, specificity of 96.6 % and accuracy of 96.8 % as compared to the manual segmentation. In addition, the obtained distance using Hausdorff is 14.2 pixels and similarity index by Jaccard is 91 %, which are indicating the enhanced performance whilst segmented area of ATFL region obtained from five normal (average Pixels—16,345.09), five tear (average Pixels—14,940.96) and five thickened (average Pixels—12,179.20) subjects’ datasets show good performance of developed framework to be used in clinical practices. Conclusions On the basis of obtained results, the developed framework is computationally more efficient and more accurate with lowest rate of coefficient of variation (less than 5 %) that indicates the highest clinical significance of this research in the assessment of ATFL injuries.
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Affiliation(s)
- Vedpal Singh
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak Darul Ridzuan, Malaysia.
| | - Irraivan Elamvazuthi
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak Darul Ridzuan, Malaysia.
| | - Varun Jeoti
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak Darul Ridzuan, Malaysia.
| | - John George
- Research Imaging Centre, University of Malaya, Kuala Lumpur, 50603, Malaysia.
| | - Akshya Swain
- Department of Electrical and Computer Engineering, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
| | - Dileep Kumar
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak Darul Ridzuan, Malaysia.
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Porée J, Garcia D, Chayer B, Ohayon J, Cloutier G. Noninvasive Vascular Elastography With Plane Strain Incompressibility Assumption Using Ultrafast Coherent Compound Plane Wave Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2618-2631. [PMID: 26625341 DOI: 10.1109/tmi.2015.2450992] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Plane strain tensor estimation using non-invasive vascular ultrasound elastography (NIVE) can be difficult to achieve using conventional focus beamforming due to limited lateral resolution and frame rate. Recent developments in compound plane wave (CPW) imaging have led to high speed and high resolution imaging. In this study, we present the performance of NIVE using coherent CPW. We show the impact of CPW beamforming on strain estimates compared to conventional focus sequences. To overcome the inherent variability of lateral strains, associated with the low lateral resolution of linear array transducers, we use the plane strain incompressibility to constrain the estimator. Taking advantage of the approximate tenfold increase in frame rate of CPW compared with conventional focus imaging, we introduce a time-ensemble estimation approach to further improve the elastogram quality. By combining CPW imaging with the constrained Lagrangian speckle model estimator, we observe an increase in elastography quality (∼ 10 dB both in signal-to-noise and contrast-to-noise ratios) over a wide range of applied strains (0.02 to 3.2%).
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23
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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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Abran M, Cloutier G, Cardinal MHR, Chayer B, Tardif JC, Lesage F. Development of a photoacoustic, ultrasound and fluorescence imaging catheter for the study of atherosclerotic plaque. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:696-703. [PMID: 25350946 DOI: 10.1109/tbcas.2014.2360560] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Atherosclerotic cardiovascular diseases are a major cause of death in industrialized countries. Molecular imaging modalities are increasingly recognized to be a promising avenue towards improved diagnosis and for the evaluation of new drug therapies. In this work, we present an acquisition system and associated catheter enabling simultaneous photoacoustic, ultrasound and fluorescence imaging of arteries designed for in vivo imaging. The catheter performance is evaluated in tissue-mimicking phantoms. Simultaneous imaging with three modalities is demonstrated at frame rates of 30 images per second for ultrasound and fluorescence and 1 image per 13 seconds for photoacoustic. Acquired radio-frequency ultrasound data could be processed to obtain radial strain elastograms. With motorized pullback, 3D imaging of phantoms was performed using the three modalities.
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Keshavarz-Motamed Z, Saijo Y, Majdouline Y, Riou L, Ohayon J, Cloutier G. Coronary artery atherectomy reduces plaque shear strains: An endovascular elastography imaging study. Atherosclerosis 2014; 235:140-9. [DOI: 10.1016/j.atherosclerosis.2014.04.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Revised: 04/16/2014] [Accepted: 04/16/2014] [Indexed: 01/26/2023]
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