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Xia M, Yang H, Huang Y, Qu Y, Zhou G, Zhang F, Wang Y, Guo Y. 3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation. Phys Med Biol 2023; 68. [PMID: 36745930 DOI: 10.1088/1361-6560/acb988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences.Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction.Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%.Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.
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Affiliation(s)
- Menghua Xia
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China
| | - Hongbo Yang
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Yi Huang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China
| | - Yanan Qu
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Guohui Zhou
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
| | - Feng Zhang
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Yuanyuan Wang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
| | - Yi Guo
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
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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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Xia M, Yang H, Huang Y, Qu Y, Guo Y, Zhou G, Zhang F, Wang Y. AwCPM-Net: A Collaborative Constraint GAN for 3D Coronary Artery Reconstruction in Intravascular Ultrasound Sequences. IEEE J Biomed Health Inform 2022; 26:3047-3058. [PMID: 35104236 DOI: 10.1109/jbhi.2022.3147888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
3D coronary artery reconstruction (3D-CAR) in intravascular ultrasound (IVUS) sequences allows quantitative analyses of vessel properties. Existing methods treat two main tasks of the 3D-CAR separately, including the cardiac phase retrieval (CPR) and the membrane border extraction (MBE). They ignore the CPR-MBE connection that could achieve mutual promotions to both tasks. In this paper, we pioneer to achieve one-step 3D-CAR via a collaborative constraint generative adversarial network (GAN) named the AwCPM-Net. The AwCPM-Net consists of a dual-task collaborative generator and a dual-task constraint discriminator. The generator combines a self-supervised CPR branch with a semi-supervised MBE branch via a warming-up connection. The discriminator promotes dual-branch predictions simultaneously. The CPR branch requires no annotations and outputs inter-frame deformation fields used for identifying cardiac phases. Deformation fields are additionally constrained by the MBE branch and the discriminator. The MBE branch predicts membrane boundaries for each frame. Two aspects assist the semi-supervised segmentation: annotation augmentation by deformation fields of the CPR branch; information exploitation on unlabeled images enabled by GAN design. Trained and tested on an IVUS dataset acquired from atherosclerosis patients, the AwCPM-Net is effective in both CPR and MBE tasks, superior to state-of-the-art IVUS CPR or MBE methods. Hence, the AwCPM-Net reconstructs reliable 3D artery anatomy in the IVUS modality.
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Blanco PJ, Ziemer PGP, Bulant CA, Ueki Y, Bass R, Räber L, Lemos PA, García-García HM. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. Med Image Anal 2021; 75:102262. [PMID: 34670148 DOI: 10.1016/j.media.2021.102262] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 10/20/2022]
Abstract
Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.
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Affiliation(s)
- Pablo J Blanco
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil.
| | - Paulo G P Ziemer
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Carlos A Bulant
- Consejo Nacional de Investigaciones Científicas, CONICET, Argentina; Universidad Nacional del Centro, UNICEN, Tandil, Argentina; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Yasushi Ueki
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ronald Bass
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA
| | - Lorenz Räber
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pedro A Lemos
- Hospital Israelita Albert Einstein, São Paulo, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Héctor M García-García
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA.
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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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Tong J, Li K, Lin W, Shudong X, Anwar A, Jiang L. Automatic lumen border detection in IVUS images using dictionary learning and kernel sparse representation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Li K, Tong J, Zhu X, Xia S. Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features. ULTRASONIC IMAGING 2021; 43:59-73. [PMID: 33448256 DOI: 10.1177/0161734620987288] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.
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Affiliation(s)
- Kai Li
- Zhejiang Sci-Tech University, Hangzhou, China
| | - Jijun Tong
- Zhejiang Sci-Tech University, Hangzhou, China
| | - Xinjian Zhu
- Zhejiang University School of Medicine, Yiwu, China
| | - Shudong Xia
- Zhejiang University School of Medicine, Yiwu, 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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Gao Z, Chung J, Abdelrazek M, Leung S, Hau WK, Xian Z, Zhang H, Li S. Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1524-1534. [PMID: 31715563 DOI: 10.1109/tmi.2019.2952939] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra- and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.
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Lee Y, Veerubhotla K, Jeong MH, Lee CH. Deep Learning in Personalization of Cardiovascular Stents. J Cardiovasc Pharmacol Ther 2020; 25:110-120. [DOI: 10.1177/1074248419878405] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principles of DL algorithms, existing DL software, and designing strategies of DL models. Latest progresses in cardiovascular devices, especially DL-based cardiovascular stent used for angioplasty, differential and advanced diagnostic means, and the treatment outcomes involved with coronary artery disease (CAD), are discussed. Also presented is DL-based discovery of new materials and future medical technologies that will facilitate the development of tailored and personalized treatment strategies by identifying and forecasting individual impending risks of cardiovascular diseases.
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Affiliation(s)
- Yugyung Lee
- School of Computing and Engineering, University of Missouri-Kansas City, MO, USA
| | - Krishna Veerubhotla
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, MO, USA
| | - Myung Ho Jeong
- Department of Cardiovascular Medicine of Chonnam National University, Gwang-Ju, South Korea
| | - Chi H. Lee
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, MO, USA
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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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14
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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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15
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Shah A, Abámoff MD, Wu X. Optimal surface segmentation with convex priors in irregularly sampled space. Med Image Anal 2019; 54:63-75. [PMID: 30836307 DOI: 10.1016/j.media.2019.02.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 01/29/2019] [Accepted: 02/07/2019] [Indexed: 12/23/2022]
Abstract
Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations.
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Affiliation(s)
- Abhay Shah
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA
| | - Michael D Abámoff
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, 52242, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA; Department of Radiation Oncology, University of Iowa, Iowa City, IA, 52242, USA.
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16
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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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17
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Balocco S, Ciompi F, Rigla J, Carrillo X, Mauri J, Radeva P. Assessment of intracoronary stent location and extension in intravascular ultrasound sequences. Med Phys 2018; 46:484-493. [PMID: 30383304 DOI: 10.1002/mp.13273] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 10/11/2018] [Accepted: 10/12/2018] [Indexed: 11/06/2022] Open
Abstract
PURPOSE An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. METHODS The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. RESULTS The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. CONCLUSIONS Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method.
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Affiliation(s)
- Simone Balocco
- Department of Matematics and Informatics, University of Barcelona, Gran Via 585, 08007, Barcelona, Spain.,Computer Vision Center, 08193, Bellaterra, Spain
| | - Francesco Ciompi
- Department of Pathology University Medical Center, Nijmegen, The Netherlands.,Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Xavier Carrillo
- University Hospital Germans Trias i Pujol, 08916, Badalona, Spain
| | - Josepa Mauri
- University Hospital Germans Trias i Pujol, 08916, Badalona, Spain
| | - Petia Radeva
- Department of Matematics and Informatics, University of Barcelona, Gran Via 585, 08007, Barcelona, Spain.,Computer Vision Center, 08193, Bellaterra, Spain
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18
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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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19
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Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8091632] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.
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20
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China D, Illanes A, Poudel P, Friebe M, Mitra P, Sheet D. Anatomical Structure Segmentation in Ultrasound Volumes Using Cross Frame Belief Propagating Iterative Random Walks. IEEE J Biomed Health Inform 2018; 23:1110-1118. [PMID: 30113902 DOI: 10.1109/jbhi.2018.2864896] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Ultrasound (US) is widely used as a low-cost alternative to computed tomography or magnetic resonance and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, a random forest learning model, and a gradient vector flow (GVF) based interframe belief propagation technique for achieving cross-frame volumetric segmentation. At the start, a weak estimate of the tissue structure is obtained using estimates of parameters of a statistical mechanics model of US tissue interaction. Ensemble learning of these parameters further using a random forest is used to initialize the segmentation pipeline. IRW is used for correcting the contour in various steps of the algorithm. Subsequently, a GVF-based interframe belief propagation is applied to adjacent frames based on the initialization of contour using information in the current frame to segment the complete volume by frame-wise processing. We have experimentally evaluated our approach using two different datasets. Intravascular ultrasound (IVUS) segmentation was evaluated using 10 pullbacks acquired at 20 MHz and thyroid US segmentation is evaluated on 16 volumes acquired at [Formula: see text] MHz. Our approach obtains a Jaccard score of [Formula: see text] for IVUS segmentation and [Formula: see text] for thyroid segmentation while processing each frame in [Formula: see text] for the IVUS and in [Formula: see text] for thyroid segmentation without the need of any computing accelerators such as GPUs.
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21
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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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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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23
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Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.12.048] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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24
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Ciompi F, Balocco S, Rigla J, Carrillo X, Mauri J, Radeva P. Computer-aided detection of intracoronary stent in intravascular ultrasound sequences. Med Phys 2016; 43:5616. [DOI: 10.1118/1.4962927] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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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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Rueda S, Knight CL, Papageorghiou AT, Noble JA. Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step. Med Image Anal 2015; 26:30-46. [PMID: 26319973 PMCID: PMC4686006 DOI: 10.1016/j.media.2015.07.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Revised: 05/28/2015] [Accepted: 07/11/2015] [Indexed: 11/24/2022]
Abstract
Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice.
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Affiliation(s)
- Sylvia Rueda
- Centre of Excellence in Personalised Healthcare, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, OX3 7DQ Oxford, UK.
| | - Caroline L Knight
- Centre of Excellence in Personalised Healthcare, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, OX3 7DQ Oxford, UK; Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, U.K
| | - Aris T Papageorghiou
- Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, U.K; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - J Alison Noble
- Centre of Excellence in Personalised Healthcare, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, OX3 7DQ Oxford, UK
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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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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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29
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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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Timmins LH, Suever JD, Eshtehardi P, McDaniel MC, Oshinski JN, Samady H, Giddens DP. Framework to co-register longitudinal virtual histology-intravascular ultrasound data in the circumferential direction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1989-1996. [PMID: 23797242 DOI: 10.1109/tmi.2013.2269275] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Considerable efforts have been directed at identifying prognostic markers for rapidly progressing coronary atherosclerotic lesions that may advance into a high-risk (vulnerable) state. Intravascular ultrasound (IVUS) has become a valuable clinical tool to study the natural history of coronary artery disease (CAD). While prospectively IVUS studies have provided tremendous insight on CAD progression, and its association with independent markers (e.g., wall shear stress), they are limited by the inability to examine the focal association between spatially heterogeneous variables (in both circumferential and axial directions). Herein, we present a framework to automatically co-register longitudinal (in-time) virtual histology-intravascular ultrasound (VH-IVUS) imaging data in the circumferential direction (i.e., rotate follow-up image so circumferential basis coincides with corresponding baseline image). Multivariate normalized cross correlation was performed on paired images (n = 636) from five patients using three independent VH-IVUS defined parameters: artery thickness, VH-IVUS defined plaque constituents, and VH-IVUS perivascular imaging data. Results exhibited high correlation between co-registration rotation angles determined automatically versus manually by an expert reader ( r(2) = 0.90). Furthermore, no significant difference between automatic and manual co-registration angles was observed ( 91.31 ±1.04(°) and 91.07 ±1.04(°), respectively; p = 0.48) and Bland-Altman analysis yielded excellent agreement ( bias = 0.24(°), 95% CI +/- 16.33(°)). In conclusion, we have developed, verified, and validated an algorithm that automatically co-registers VH-IVUS imaging data that will allow for the focal examination of CAD progression.
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31
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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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Alberti M, Balocco S, Carrillo X, Mauri J, Radeva P. Automatic non-rigid temporal alignment of intravascular ultrasound sequences: method and quantitative validation. ULTRASOUND IN MEDICINE & BIOLOGY 2013; 39:1698-1712. [PMID: 23791349 DOI: 10.1016/j.ultrasmedbio.2013.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 02/26/2013] [Accepted: 03/05/2013] [Indexed: 06/02/2023]
Abstract
Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages.
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Affiliation(s)
- Marina Alberti
- Department of Applied Mathematics and Analysis, University of Barcelona, Barcelona, Spain.
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Learning to Detect Stent Struts in Intravascular Ultrasound. PATTERN RECOGNITION AND IMAGE ANALYSIS 2013. [DOI: 10.1007/978-3-642-38628-2_68] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2022]
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Balocco S, Gatta C, Alberti M, Carrillo X, Rigla J, Radeva P. Relation between plaque type, plaque thickness, blood shear stress, and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound. Med Phys 2012; 39:7430-45. [DOI: 10.1118/1.4760993] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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