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Yang Y, Husmeier D, Gao H, Berry C, Carrick D, Radjenovic A. Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. Comput Med Imaging Graph 2024; 113:102333. [PMID: 38281420 DOI: 10.1016/j.compmedimag.2024.102333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/15/2023] [Accepted: 12/26/2023] [Indexed: 01/30/2024]
Abstract
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) can be used as a non-invasive method for the assessment of myocardial perfusion. The acquired images can be utilised to analyse the spatial extent and severity of myocardial ischaemia (regions with impaired microvascular blood flow). In the present paper, we propose a novel generalisable spatio-temporal hierarchical Bayesian model (GST-HBM) to automate the detection of ischaemic lesions and improve the in silico prediction accuracy by systematically integrating spatio-temporal context information. We present a computational inference procedure with an adequate trade-off between accuracy and computational efficiency, whereby model parameters are sampled from the posterior distribution with Gibbs sampling, while lower-level hyperparameters are selected using model selection strategies based on the Watanabe Akaike information criterion (WAIC). We have assessed our method on both synthetic (in silico) data with known gold-standard and 12 sets of clinical first-pass myocardial perfusion DCE-MRI datasets. We have also carried out a comparative performance evaluation with four established alternative methods: Gaussian mixture model (GMM), opening and closing operations based on Gaussian mixture model (GMMC&Omax), Markov random field constrained Gaussian mixture model (GMM-MRF) and model-based hierarchical Bayesian model (M-HBM). Our results show that the proposed GST-HBM method achieves much higher in silico prediction accuracy than the established alternative methods. Furthermore, this method appears to provide a more robust delineation of ischaemic lesions in datasets affected by spatially variant noise.
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Affiliation(s)
- Yalei Yang
- School of Mathematics & Statistics, University of Glasgow, University Place, Glasgow, G12 8QQ, United Kingdom; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Dirk Husmeier
- School of Mathematics & Statistics, University of Glasgow, University Place, Glasgow, G12 8QQ, United Kingdom.
| | - Hao Gao
- School of Mathematics & Statistics, University of Glasgow, University Place, Glasgow, G12 8QQ, United Kingdom
| | - Colin Berry
- School of Cardiovascular & Metabolic Health, University of Glasgow, BHF Glasgow Cardiovascular Research Centre (GCRC), 126 University Place, Glasgow, G12 8TA, United Kingdom
| | - David Carrick
- University Hospital Hairmyres, 218 Eaglesham Rd, East Kilbride, Glasgow G75 8RG, United Kingdom
| | - Aleksandra Radjenovic
- School of Cardiovascular & Metabolic Health, University of Glasgow, BHF Glasgow Cardiovascular Research Centre (GCRC), 126 University Place, Glasgow, G12 8TA, United Kingdom.
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2
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Sun S, Wang Y, Yang J, Feng Y, Tang L, Liu S, Ning H. Topology-sensitive weighting model for myocardial segmentation. Comput Biol Med 2023; 165:107286. [PMID: 37633088 DOI: 10.1016/j.compbiomed.2023.107286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/12/2023] [Accepted: 07/28/2023] [Indexed: 08/28/2023]
Abstract
Accurate myocardial segmentation is crucial for the diagnosis of various heart diseases. However, segmentation results often suffer from topology structural errors, such as broken connections and holes, especially in cases of poor image quality. These errors are unacceptable in clinical diagnosis. We proposed a Topology-Sensitive Weight (TSW) model to keep both pixel-wise accuracy and topological correctness. Specifically, the Position Weighting Update (PWU) strategy with the Boundary-Sensitive Topology (BST) module can guide the model to focus on positions where topological features are sensitive to pixel values. The Myocardial Integrity Topology (MIT) module can serve as a guide for maintaining myocardial integrity. We evaluate the TSW model on the CAMUS dataset and a private echocardiography myocardial segmentation dataset. The qualitative and quantitative experimental results show that the TSW model significantly enhances topological accuracy while maintaining pixel-wise precision.
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Affiliation(s)
- Song Sun
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China
| | - Yonghuai Wang
- Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Yong Feng
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Lingzhi Tang
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Shuo Liu
- Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, Shenyang, China
| | - Hongxia Ning
- Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, Shenyang, China
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3
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Zhang Y, Wang F, Wu H, Yang Y, Xu W, Wang S, Chen W, Lu L. An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107267. [PMID: 36502547 DOI: 10.1016/j.cmpb.2022.107267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/16/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVES We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated 13N-NH3 PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment. METHODS Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD. RESULTS Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 (P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth (r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair (r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194. CONCLUSION The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.
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Affiliation(s)
- Yangmei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Fanghu Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Huiqin Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yuling Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Weiping Xu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shuxia Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China; Pazhou Lab, Guangzhou, China.
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Pednekar AS, Cheong BYC, Muthupillai R. Ultrafast Computation of Left Ventricular Ejection Fraction by Using Temporal Intensity Variation in Cine Cardiac Magnetic Resonance. Tex Heart Inst J 2021; 48:471806. [PMID: 34643734 DOI: 10.14503/thij-20-7238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Cardiac magnetic resonance enables comprehensive cardiac evaluation; however, intense time and labor requirements for data acquisition and processing have discouraged many clinicians from using it. We have developed an alternative image-processing algorithm that requires minimal user interaction: an ultrafast algorithm that computes left ventricular ejection fraction (LVEF) by using temporal intensity variation in cine balanced steady-state free precession (bSSFP) short-axis images, with or without contrast medium. We evaluated the algorithm's performance against an expert observer's analysis for segmenting the LV cavity in 65 study participants (LVEF range, 12%-70%). In 12 instances, contrast medium was administered before cine imaging. Bland-Altman analysis revealed quantitative effects of LV basal, midcavity, and apical morphologic variation on the algorithm's accuracy. Total computation time for the LV stack was <2.5 seconds. The algorithm accurately delineated endocardial boundaries in 1,132 of 1,216 slices (93%). When contours in the extreme basal and apical slices were not adequate, they were replaced with manually drawn contours. The Bland-Altman mean differences were <1.2 mL (0.8%) for end-diastolic volume, <5 mL (6%) for end-systolic volume, and <3% for LVEF. Standard deviation of the difference was ≤4.1% of LV volume for all sections except the midcavity in end-systole (8.3% of end-systolic volume). We conclude that temporal intensity variation-based ultrafast LVEF computation is clinically accurate across a range of LV shapes and wall motions and is suitable for postcontrast cine SSFP imaging. Our algorithm enables real-time processing of cine bSSFP images on a commercial scanner console within 3 seconds in an unobtrusive automated process.
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Affiliation(s)
| | - Benjamin Y C Cheong
- Department of Radiology, CHI St. Luke's Health-Baylor St. Luke's Medical Center, Houston, Texas.,Department of Cardiology, Texas Heart Institute, Houston, Texas
| | - Raja Muthupillai
- Department of Radiology, CHI St. Luke's Health-Baylor St. Luke's Medical Center, Houston, Texas.,Department of Cardiology, Texas Heart Institute, Houston, Texas
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Irshad M, Sharif M, Yasmin M, Rehman A, Khan MA. Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours. Microsc Res Tech 2021; 85:308-323. [PMID: 34418197 DOI: 10.1002/jemt.23906] [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: 02/27/2021] [Revised: 06/06/2021] [Accepted: 08/02/2021] [Indexed: 11/06/2022]
Abstract
Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.
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Affiliation(s)
- Mehreen Irshad
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Amjad Rehman
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
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Merjulah R, Chandra J. An Integrated Segmentation Techniques for Myocardial Ischemia. PATTERN RECOGNITION AND IMAGE ANALYSIS 2020. [DOI: 10.1134/s1054661820030190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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7
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Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis. MATHEMATICS 2020. [DOI: 10.3390/math8091511] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort.
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8
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Cardenas R, Curiale AH, Mato G. Left ventricle segmentation using a Bayesian approach with distance dependent shape priors. Biomed Phys Eng Express 2020; 6:045013. [PMID: 33444274 DOI: 10.1088/2057-1976/ab9556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.
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Affiliation(s)
- Rodrigo Cardenas
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. Centro Atómico Bariloche, Av. Bustillo 9500, R8402AGP S. C. de Bariloche, Río Negro, Argentina
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9
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Curiale AH, Colavecchia FD, Mato G. Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 169:37-50. [PMID: 30638590 DOI: 10.1016/j.cmpb.2018.12.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 11/16/2018] [Accepted: 12/10/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVE This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). METHODS The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function. RESULTS The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation ( ∼ 0.9 Dice's coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output. CONCLUSION Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both diagnosis and treatment of different pathologies. SIGNIFICANCE This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring.
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Affiliation(s)
- Ariel H Curiale
- CONICET - Departamento de Física Médica, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina. http://www.curiale.com.ar
| | - Flavio D Colavecchia
- CONICET - Centro Integral de Medicina Nuclear y Radioterapia, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina; Comisión Nacional de Energía Atómica (CNEA) Argentina
| | - German Mato
- CONICET - Departamento de Física Médica, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina; Comisión Nacional de Energía Atómica (CNEA) Argentina
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10
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Romaguera LV, Romero FP, Fernandes Costa Filho CF, Fernandes Costa MG. Myocardial segmentation in cardiac magnetic resonance images using fully convolutional neural networks. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.04.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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Queirós S, Vilaça JL, Morais P, Fonseca JC, D'hooge J, Barbosa D. Fast left ventricle tracking using localized anatomical affine optical flow. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33. [PMID: 28208231 DOI: 10.1002/cnm.2871] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 02/12/2017] [Indexed: 06/06/2023]
Abstract
In daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.
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Affiliation(s)
- Sandro Queirós
- ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Lab on Cardiovascular Imaging and Dynamics, Dept. of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - João L Vilaça
- ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal
- DIGARC-Polytechnic Institute of Cávado and Ave (IPCA), Barcelos, Portugal
| | - Pedro Morais
- ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Lab on Cardiovascular Imaging and Dynamics, Dept. of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- INEGI, Faculty of Engineering, University of Porto, Porto, Portugal
| | - Jaime C Fonseca
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Jan D'hooge
- Lab on Cardiovascular Imaging and Dynamics, Dept. of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Daniel Barbosa
- ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal
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Oksuz I, Mukhopadhyay A, Dharmakumar R, Tsaftaris SA. Unsupervised Myocardial Segmentation for Cardiac BOLD. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2228-2238. [PMID: 28708550 PMCID: PMC5726889 DOI: 10.1109/tmi.2017.2726112] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace. Our method is based on variational pre-processing and spatial regularization using Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase-resolved BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across ten canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns.
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Affiliation(s)
- Ilkay Oksuz
- IMT School for Advanced Studies Lucca, Italy () and also with Diagnostic Radiology Department of Yale University, CT, USA
| | - Anirban Mukhopadhyay
- Interactive Graphics Systems Group, Technische Universitat Darmstadt, Darmstadt, Germany, ()
| | - Rohan Dharmakumar
- Cedars-Sinai Medical Center and University of California Los Angeles, CA, USA ()
| | - Sotirios A. Tsaftaris
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK. ()
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13
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A 3D Hermite-based multiscale local active contour method with elliptical shape constraints for segmentation of cardiac MR and CT volumes. Med Biol Eng Comput 2017; 56:833-851. [PMID: 29058109 DOI: 10.1007/s11517-017-1732-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 10/04/2017] [Indexed: 10/18/2022]
Abstract
Analysis of cardiac images is a fundamental task to diagnose heart problems. Left ventricle (LV) is one of the most important heart structures used for cardiac evaluation. In this work, we propose a novel 3D hierarchical multiscale segmentation method based on a local active contour (AC) model and the Hermite transform (HT) for LV analysis in cardiac magnetic resonance (MR) and computed tomography (CT) volumes in short axis view. Features such as directional edges, texture, and intensities are analyzed using the multiscale HT space. A local AC model is configured using the HT coefficients and geometrical constraints. The endocardial and epicardial boundaries are used for evaluation. Segmentation of the endocardium is controlled using elliptical shape constraints. The final endocardial shape is used to define the geometrical constraints for segmentation of the epicardium. We follow the assumption that epicardial and endocardial shapes are similar in volumes with short axis view. An initialization scheme based on a fuzzy C-means algorithm and mathematical morphology was designed. The algorithm performance was evaluated using cardiac MR and CT volumes in short axis view demonstrating the feasibility of the proposed method.
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14
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Albà X, Lekadir K, Pereañez M, Medrano-Gracia P, Young AA, Frangi AF. Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med Image Anal 2017; 43:129-141. [PMID: 29073531 DOI: 10.1016/j.media.2017.10.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 08/29/2017] [Accepted: 10/04/2017] [Indexed: 01/09/2023]
Abstract
Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The results obtained based on over 1200 cases from the Cardiac Atlas Project show the promise of fully automatic initialization and quality control for population studies.
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Affiliation(s)
- Xènia Albà
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain.
| | - Karim Lekadir
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain
| | - Marco Pereañez
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, NZ
| | - Alejandro F Frangi
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Electronic and Electrical Engineering Department, University of Sheffield, Sheffield, UK
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15
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Abstract
Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments' results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.
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Affiliation(s)
- Ali A Kiaei
- Department of computer engineering, Bu-Ali Sina University, Hamedan, Iran.
| | - Hassan Khotanlou
- Department of computer engineering, Bu-Ali Sina University, Hamedan, Iran.
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Guo Y, Du GQ, Xue JY, Xia R, Wang YH. A novel myocardium segmentation approach based on neutrosophic active contour model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:109-116. [PMID: 28325439 DOI: 10.1016/j.cmpb.2017.02.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 02/09/2017] [Accepted: 02/15/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES Automatic delineation of the myocardium in echocardiography can assist radiologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise. The purpose of this study is to automatically detect myocardium region in left ventricle myocardial contrast echocardiography (LVMCE) images to help radiologists' diagnosis and further measurement on infarction size. METHODS The LVMCE image is firstly mapped into neutrosophic similarity (NS) domain using the intensity and homogeneity features. Then, a neutrosophic active contour model (NACM) is proposed and the energy function is defined by the NS values. Finally, the ventricle is detected using the curve evolving results. The ventricle's boundary is identified as the endocardium. To speed up the evolution procedure and increase the detection accuracy, a clustering algorithm is employed to obtain the initial ventricle region. The curve evolution procedure in NACM is utilized again to obtain the epicardium, where the initial contour uses the detected endocardium and the anatomy knowledge on the thickness of the myocardium. RESULTS Echocardiographic studies are performed on 10 male Sprague-Dawley rats using a Vivid 7 system including 5 normal cases and 5 rats with myocardial infarction. The myocardium boundaries manually outlined by an experienced radiologist are used as the reference standard for the performance evaluation. Two metrics, Hdist and AvgDist, are employed to evaluate the detection results. The NACM method was compared with those from the eliminated particle swarm optimization (EPSO) and active contour model without edges (ACMWE) methods. The mean and standard deviation of the Hdist and AvgDist on endocardium are 6.83 ± 1.12mm and 0.79 ± 0.28mm using EPSO method, 7.12 ± 0.98mm and 0.82 ± 0.32mm using ACMWE method, and 4.55 ± 0.9mm and 0.58 ± 0.18mm using NACM method, respectively. The improvement on epicardium is much more significant, and two metrics are decreased from 7.45 ± 1.24mm, and 1.47 ± 0.34mm using EPSO method, and 8.21±0.43mm, and 1.73±0.47mm using ACMWE method, to 4.94 ± 0.82mm, and 0.84 ± 0.22mm using NACM method, respectively. CONCLUSIONS The proposed method can automatically detect myocardium accurately, and is helpful for clinical therapeutics to measure myocardial perfusion and infarct size.
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Affiliation(s)
- Yanhui Guo
- Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA.
| | - Guo-Qing Du
- Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jing-Yi Xue
- Department of Cardiology, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Rong Xia
- Oracle Corporation, Westminster, CO, USA
| | - Yu-Hang Wang
- Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, Harbin, China
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17
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Qian X, Lin Y, Zhao Y, Wang J, Liu J, Zhuang X. Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method. Med Phys 2016; 42:1424-35. [PMID: 25735296 DOI: 10.1118/1.4907993] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Myocardium segmentation in cardiac magnetic resonance (MR) images plays a vital role in clinical diagnosis of the cardiovascular diseases. Because of the low contrast and large variation in intensity and shapes, myocardium segmentation has been a challenging task. A dynamic programming (DP) based segmentation method, incorporating the likelihood and shape information of the myocardium, is developed for segmenting myocardium in cardiac MR images. METHODS The endocardium, i.e., the left ventricle blood cavity, is segmented for initialization, and then the optimal epicardium contour is determined using the polar-transformed image and DP scheme. In the DP segmentation scheme, three techniques are proposed to improve the segmentation performance: (1) the likelihood image of the myocardium is constructed to define the external cost in the DP, thus the cost function incorporates prior probability estimation; (2) the adaptive search range is introduced to determine the polar-transformed image, thereby excluding irrelevant tissues; (3) the connectivity constrained DP algorithm is developed to obtain an optimal closed contour. Four metrics, including the Dice metric (Dice), root mean squared error (RMSE), reliability, and correlation coefficient, are used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on a private dataset and the MICCAI 2009 challenge dataset. The authors also explored the effects of the three new techniques of the DP scheme in the proposed method. RESULTS For the qualitative evaluation, the segmentation results of the proposed method were clinically acceptable. For the quantitative evaluation, the mean (Dice) for the endocardium and epicardium was 0.892 and 0.927, respectively; the mean RMSE was 2.30 mm for the endocardium and 2.39 mm for the epicardium. In addition, the three new techniques in the proposed DP scheme, i.e., the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm, improved the segmentation performance for the epicardium with 0.029, 0.047, and 0.007 in terms of the Dice and 0.98, 1.31, and 0.21 mm in terms of the RMSE, respectively. CONCLUSIONS The three techniques (the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm) can improve the segmentation ability of the DP method, and the proposed method with these techniques has the ability to achieve the acceptable segmentation result of the myocardium in cardiac MR images. Therefore, the proposed method would be useful in clinical diagnosis of the cardiovascular diseases.
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Affiliation(s)
- Xiaohua Qian
- SJTUCU International Cooperative Research Center, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China and Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
| | - Yuan Lin
- Division of Research and Innovations, Carestream Health, Inc., Rochester, New York 14615
| | - Yue Zhao
- College of Electronic Science and Engineering, Jilin University, 2699 Qianjing Street, Changchun, Jilin 130012, China
| | - Jing Wang
- Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Jing Liu
- Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
| | - Xiahai Zhuang
- SJTUCU International Cooperative Research Center, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Feng C, Zhang S, Zhao D, Li C. Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Med Phys 2016; 43:2741-2755. [DOI: 10.1118/1.4947126] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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19
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Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2016; 29:155-95. [PMID: 26811173 PMCID: PMC4830888 DOI: 10.1007/s10334-015-0521-4] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.
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Affiliation(s)
- Peng Peng
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | | | - Ali Gooya
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | - Ling Shao
- Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Steffen E Petersen
- Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
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20
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Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 2016; 30:108-119. [PMID: 26917105 DOI: 10.1016/j.media.2016.01.005] [Citation(s) in RCA: 282] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 01/03/2016] [Accepted: 01/18/2016] [Indexed: 10/22/2022]
Abstract
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively.
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Affiliation(s)
- M R Avendi
- Center for Pervasive Communications and Computing, University of California, Irvine, USA; The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, USA.
| | - Arash Kheradvar
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, USA.
| | - Hamid Jafarkhani
- Center for Pervasive Communications and Computing, University of California, Irvine, USA.
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21
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Varga-Szemes A, Muscogiuri G, Schoepf UJ, Wichmann JL, Suranyi P, De Cecco CN, Cannaò PM, Renker M, Mangold S, Fox MA, Ruzsics B. Clinical feasibility of a myocardial signal intensity threshold-based semi-automated cardiac magnetic resonance segmentation method. Eur Radiol 2015; 26:1503-11. [PMID: 26267520 DOI: 10.1007/s00330-015-3952-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 07/15/2015] [Accepted: 07/28/2015] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To assess the accuracy and efficiency of a threshold-based, semi-automated cardiac MRI segmentation algorithm in comparison with conventional contour-based segmentation and aortic flow measurements. METHODS Short-axis cine images of 148 patients (55 ± 18 years, 81 men) were used to evaluate left ventricular (LV) volumes and mass (LVM) using conventional and threshold-based segmentations. Phase-contrast images were used to independently measure stroke volume (SV). LV parameters were evaluated by two independent readers. RESULTS Evaluation times using the conventional and threshold-based methods were 8.4 ± 1.9 and 4.2 ± 1.3 min, respectively (P < 0.0001). LV parameters measured by the conventional and threshold-based methods, respectively, were end-diastolic volume (EDV) 146 ± 59 and 134 ± 53 ml; end-systolic volume (ESV) 64 ± 47 and 59 ± 46 ml; SV 82 ± 29 and 74 ± 28 ml (flow-based 74 ± 30 ml); ejection fraction (EF) 59 ± 16 and 58 ± 17%; and LVM 141 ± 55 and 159 ± 58 g. Significant differences between the conventional and threshold-based methods were observed in EDV, ESV, and LVM mesurements; SV from threshold-based and flow-based measurements were in agreement (P > 0.05) but were significantly different from conventional analysis (P < 0.05). Excellent inter-observer agreement was observed. CONCLUSIONS Threshold-based LV segmentation provides improved accuracy and faster assessment compared to conventional contour-based methods. KEY POINTS • Threshold-based left ventricular segmentation provides time-efficient assessment of left ventricular parameters • The threshold-based method can discriminate between blood and papillary muscles • This method provides improved accuracy compared to aortic flow measurements as a reference.
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Affiliation(s)
- Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA
| | - Giuseppe Muscogiuri
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.,Department of Medical-Surgical Sciences and Translational Medicine, University of Rome "Sapienza", Rome, Italy
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
| | - Julian L Wichmann
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.,Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Pal Suranyi
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA
| | - Carlo N De Cecco
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA
| | - Paola M Cannaò
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.,Scuola di Specializzazione in Radiodiagnostica, University of Milan, Milan, Italy
| | - Matthias Renker
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.,Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Stefanie Mangold
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.,Department of Diagnostic and Interventional Radiology, Eberhard-Karls University Tuebingen, Tuebingen, Germany
| | - Mary A Fox
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA
| | - Balazs Ruzsics
- Department of Cardiology, Royal Liverpool and Broadgreen University Hospitals, Liverpool, UK
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22
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Myocardial Iron Loading Assessment by Automatic Left Ventricle Segmentation with Morphological Operations and Geodesic Active Contour on T2* images. Sci Rep 2015. [PMID: 26215336 PMCID: PMC4516984 DOI: 10.1038/srep12438] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Myocardial iron loading thalassemia patients could be identified using T2* magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2* mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2* analysis indicated that regions with intensity lower than 20 ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.
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23
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Suever JD, Wehner GJ, Haggerty CM, Jing L, Hamlet SM, Binkley CM, Kramer SP, Mattingly AC, Powell DK, Bilchick KC, Epstein FH, Fornwalt BK. Simplified post processing of cine DENSE cardiovascular magnetic resonance for quantification of cardiac mechanics. J Cardiovasc Magn Reson 2014; 16:94. [PMID: 25430079 PMCID: PMC4246464 DOI: 10.1186/s12968-014-0094-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 11/14/2014] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance using displacement encoding with stimulated echoes (DENSE) is capable of assessing advanced measures of cardiac mechanics such as strain and torsion. A potential hurdle to widespread clinical adoption of DENSE is the time required to manually segment the myocardium during post-processing of the images. To overcome this hurdle, we proposed a radical approach in which only three contours per image slice are required for post-processing (instead of the typical 30-40 contours per image slice). We hypothesized that peak left ventricular circumferential, longitudinal and radial strains and torsion could be accurately quantified using this simplified analysis. METHODS AND RESULTS We tested our hypothesis on a large multi-institutional dataset consisting of 541 DENSE image slices from 135 mice and 234 DENSE image slices from 62 humans. We compared measures of cardiac mechanics derived from the simplified post-processing to those derived from original post-processing utilizing the full set of 30-40 manually-defined contours per image slice. Accuracy was assessed with Bland-Altman limits of agreement and summarized with a modified coefficient of variation. The simplified technique showed high accuracy with all coefficients of variation less than 10% in humans and 6% in mice. The accuracy of the simplified technique was also superior to two previously published semi-automated analysis techniques for DENSE post-processing. CONCLUSIONS Accurate measures of cardiac mechanics can be derived from DENSE cardiac magnetic resonance in both humans and mice using a simplified technique to reduce post-processing time by approximately 94%. These findings demonstrate that quantifying cardiac mechanics from DENSE data is simple enough to be integrated into the clinical workflow.
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Affiliation(s)
- Jonathan D Suever
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - Gregory J Wehner
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
- />Department of Biomedical Engineering, University of Kentucky, Lexington, KY USA
| | - Christopher M Haggerty
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - Linyuan Jing
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - Sean M Hamlet
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
- />Department of Electrical Engineering, University of Kentucky, Lexington, KY USA
| | - Cassi M Binkley
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - Sage P Kramer
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - Andrea C Mattingly
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
| | - David K Powell
- />Department of Biomedical Engineering, University of Kentucky, Lexington, KY USA
| | - Kenneth C Bilchick
- />Department of Medicine, University of Virginia, Charlottesville, VA USA
| | - Frederick H Epstein
- />Department of Biomedical Engineering, University of Virginia, Charlottesville, VA USA
| | - Brandon K Fornwalt
- />Department of Pediatrics and Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY USA
- />Department of Biomedical Engineering, University of Kentucky, Lexington, KY USA
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Queirós S, Barbosa D, Heyde B, Morais P, Vilaça JL, Friboulet D, Bernard O, D’hooge J. Fast automatic myocardial segmentation in 4D cine CMR datasets. Med Image Anal 2014; 18:1115-31. [DOI: 10.1016/j.media.2014.06.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Revised: 05/05/2014] [Accepted: 06/06/2014] [Indexed: 10/25/2022]
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Arif O, Sundaramoorthi G, Hong BW, Yezzi A. Tracking using motion estimation with physically motivated inter-region constraints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1875-1889. [PMID: 24846558 DOI: 10.1109/tmi.2014.2325040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous estimate of the structures using a new physically motivated motion estimation scheme. Our method estimates motion by regularizing only within structures so that differing motions among different structures are not mixed. It simultaneously satisfies the physical constraints at the interface between a fluid and a medium that the normal component of the fluid's motion must match the normal component of the medium's motion and the No-Slip condition, which states that the tangential velocity approaches zero near the interface. We show that these conditions lead to partial differential equations with Robin boundary conditions at the interface, which couple the motion between structures. We show that propagating a segmentation across frames using our motion estimation scheme leads to more accurate segmentation than traditional motion estimation that does not use physical constraints. Our method is suited to interactive segmentation, prominently used in commercial applications for cardiac analysis, where segmentation propagation is used to predict a segmentation in the next frame. We show that our method leads to more accurate predictions than a popular and recent interactive method used in cardiac segmentation.
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Dwyer MG, Bergsland N, Zivadinov R. Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model. Neuroimage 2014; 90:207-17. [DOI: 10.1016/j.neuroimage.2013.12.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 12/01/2013] [Accepted: 12/03/2013] [Indexed: 10/25/2022] Open
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Paiement A, Mirmehdi M, Xie X, Hamilton MCK. Integrated segmentation and interpolation of sparse data. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2014; 23:110-125. [PMID: 24158475 DOI: 10.1109/tip.2013.2286903] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.
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Cordero-Grande L, Merino-Caviedes S, Aja-Fernández S, Alberola-López C. Groupwise elastic registration by a new sparsity-promoting metric: application to the alignment of cardiac magnetic resonance perfusion images. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013; 35:2638-2650. [PMID: 24051725 DOI: 10.1109/tpami.2013.74] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.
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Lu YL, Connelly KA, Dick AJ, Wright GA, Radau PE. Automatic functional analysis of left ventricle in cardiac cine MRI. Quant Imaging Med Surg 2013; 3:200-9. [PMID: 24040616 PMCID: PMC3759139 DOI: 10.3978/j.issn.2223-4292.2013.08.02] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 08/02/2013] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37). MATERIALS AND METHODS The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively. RESULTS There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively). CONCLUSIONS The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.
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Affiliation(s)
- Ying-Li Lu
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Kim A. Connelly
- Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael’s Hospital and University of Toronto, Toronto, ON, Canada
- Cardiology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Alexander J. Dick
- Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Graham A. Wright
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, ON, Canada
| | - Perry E. Radau
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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Dreijer JF, Herbst BM, du Preez JA. Left ventricular segmentation from MRI datasets with edge modelling conditional random fields. BMC Med Imaging 2013; 13:24. [PMID: 23899437 PMCID: PMC3737053 DOI: 10.1186/1471-2342-13-24] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 07/25/2013] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND This paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult. METHODS The endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell's method is applied to find CRF parameters by minimizing the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimization of frame alignment error. RESULTS We present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified. CONCLUSIONS The presented model integrates shape and motion information to segment the inner and outer contours in the presence of papillary muscles. On the Sunnybrook dataset we find an average Dice metric of 0.91 ± 0.02 and 0.93 ± 0.02 for the inner and outer segmentations, respectively. Particularly problematic are patients with hypertrophy where the blood pool disappears from view at end-systole.
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Affiliation(s)
- Janto F Dreijer
- Department of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, South Africa
| | - Ben M Herbst
- Department of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa
| | - Johan A du Preez
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, South Africa
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Cordero-Grande L, Vegas-Sánchez-Ferrero G, Casaseca-de-la-Higuera P, Aja-Fernández S, Alberola-López C. A magnetic resonance software simulator for the evaluation of myocardial deformation estimation. Med Eng Phys 2013; 35:1331-40. [PMID: 23561923 DOI: 10.1016/j.medengphy.2013.03.001] [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: 01/31/2012] [Revised: 01/08/2013] [Accepted: 03/02/2013] [Indexed: 11/30/2022]
Abstract
This paper proposes a methodology to design a physiologically realistic computer simulator of images of the left ventricle myocardium based on a patient-specific biomechanical model. The simulator takes a magnetic resonance image of a given patient at end diastole, uses a manual segmentation of that image to model the geometry of the myocardium and sets the parameters of the constitutive model used for biomechanical simulation according to a regional labeling of the contractility of the myocardium for that patient. The simulated deformations are used to warp the magnetic resonance dataset throughout the cardiac cycle to generate different image modalities. The simulator is validated by quantifying its ability to model actual deformations in a set of patients affected by an acute myocardial infarction. Specifically a high correlation has been encountered between the ejection fraction derived from the simulated end systolic deformation of the myocardium and the myocardium segmented from actual data. Additionally, most of the parameters that describe the simulated deformation compare well with reported values. Overall, the simulator is intended as a testbed for extensive comparisons of myocardial motion tracking methods due to its ability to relate the impaired myocardial function with the associated ventricular remodeling, a novel contribution in the literature of cardiac image simulators.
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Affiliation(s)
- Lucilio Cordero-Grande
- Laboratorio de Procesado de Imagen, ETSIT, University of Valladolid, Paseo de Belén 15, 40011 Valladolid, Spain.
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Eslami A, Karamalis A, Katouzian A, Navab N. Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI. Med Image Anal 2013; 17:236-53. [PMID: 23313331 DOI: 10.1016/j.media.2012.10.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2012] [Revised: 10/29/2012] [Accepted: 10/31/2012] [Indexed: 11/26/2022]
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Aja-Fernández S, Cordero-Grande L, Vegas-Sanchez-Ferrero G, de Luis-García R, Alberola-López C. Robust estimation of MRI myocardial perfusion parameters. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:4382-4385. [PMID: 24110704 DOI: 10.1109/embc.2013.6610517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Myocardial perfusion imaging by first-pass contrast-enhanced magnetic resonance allows to asses the viability of a tissue by the study of the contrast agent transit through the cardiac chambers and myocardium. Since visual inspection is difficult and may left aside critical temporal information, the need of automatic quantitative analysis arises. We propose two robust estimators of the parameters that quantify the perfusion according to a prior pharmacokinetic model. The estimators are based on the concentration of the contrast agent inside the tissue and the blood.
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Faghih Roohi S, Aghaeizadeh Zoroofi R. 4D statistical shape modeling of the left ventricle in cardiac MR images. Int J Comput Assist Radiol Surg 2012; 8:335-51. [PMID: 22893114 DOI: 10.1007/s11548-012-0787-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2012] [Accepted: 07/16/2012] [Indexed: 10/28/2022]
Abstract
PURPOSE Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifacts or partial volume effect. In this paper, we construct a 4D statistical model of the left ventricle using human cardiac short-axis MR images. METHODS Kernel PCA is utilized to explore the nonlinear variation of a population. The distribution of the landmarks is divided into the inter- and intra-subject subspaces. We compare the result of Kernel PCA with linear PCA and ICA for each of these subspaces. The initial atlas in natural coordinate system is built for the end-diastolic frame. The landmarks extracted from it are propagated to all frames of all datasets. We apply the 4D KPCA-based ASM for segmentation of all phases of a cardiac cycle and compare it with the conventional ASM. RESULTS The proposed statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. We investigate the behavior of the nonlinear model for different values of the kernel parameter. The results show that the model built by KPCA is less compact than PCA but more compact than ICA. Although for a constant number of modes the reconstruction error is a little higher for the KPCA-based statistical model, it produces a statistical model with substantially better specificity than PCA- and ICA-based models. CONCLUSION Quantitative analysis of the results demonstrates that our method improves the segmentation accuracy.
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