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Ding W, Li L, Qiu J, Wang S, Huang L, Chen Y, Yang S, Zhuang X. Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3474-3486. [PMID: 37347625 DOI: 10.1109/tmi.2023.3288046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.
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2
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Singh Y, Atalla S, Mansoor W, Paul R, Deepa D. To predict the left ventricular endocardial scar tissue pattern using Radon descriptor-based machine learning. BMC Res Notes 2023; 16:185. [PMID: 37620937 PMCID: PMC10464130 DOI: 10.1186/s13104-023-06466-0] [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: 09/16/2022] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVE Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventricle (LV) segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches of 17 patients from Computer tomography (CT) images of the heart were used and categorized into "endocardial Scar tissue" and "normal tissue" groups. The ten feature vectors are extracted from patches using Radon descriptors and fed into a traditional machine learning model. RESULTS The decision tree has shown the best performance with 98.07% accuracy. This study is the first attempt to provide a Radon transform-based machine learning method to distinguish patterns between "endocardial Scar tissue" and "normal tissue" groups. Our proposed research method could be potentially used in advanced interventions.
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Affiliation(s)
- Yashbir Singh
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan.
- Department of Radiology, Mayo clinic, Rochester, MN, USA.
| | - Shadi Atalla
- Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates.
| | - Wathiq Mansoor
- Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA
| | - Deepa Deepa
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan
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3
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Zhu F, Li L, Zhao J, Zhao C, Tang S, Nan J, Li Y, Zhao Z, Shi J, Chen Z, Han C, Jiang Z, Zhou W. A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images. Comput Biol Med 2023; 160:106954. [PMID: 37130501 DOI: 10.1016/j.compbiomed.2023.106954] [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: 06/07/2022] [Revised: 04/09/2023] [Accepted: 04/15/2023] [Indexed: 05/04/2023]
Abstract
Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study, a novel method combining deep learning with shape priors was developed and validated to extract the LV myocardium and automatically measure LV functional parameters. The method integrates a three-dimensional (3D) V-Net with a shape deformation module that incorporates shape priors generated by a dynamic programming (DP) algorithm to guide its output during training. A retrospective analysis was performed on an MPS dataset comprising 31 subjects without or with mild ischemia, 32 subjects with moderate ischemia, and 12 subjects with severe ischemia. Myocardial contours were manually annotated as the ground truth. A 5-fold stratified cross-validation was used to train and validate the models. The clinical performance was evaluated by measuring LV end-systolic volume (ESV), end-diastolic volume (EDV), left ventricular ejection fraction (LVEF), and scar burden from the extracted myocardial contours. There were excellent agreements between segmentation results by our proposed model and those from the ground truth, with a Dice similarity coefficient (DSC) of 0.9573 ± 0.0244, 0.9821 ± 0.0137, and 0.9903 ± 0.0041, as well as Hausdorff distances (HD) of 6.7529 ± 2.7334 mm, 7.2507 ± 3.1952 mm, and 7.6121 ± 3.0134 mm in extracting the LV endocardium, myocardium, and epicardium, respectively. Furthermore, the correlation coefficients between LVEF, ESV, EDV, stress scar burden, and rest scar burden measured from our model results and the ground truth were 0.92, 0.958, 0.952, 0.972, and 0.958, respectively. The proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV functions.
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Affiliation(s)
- Fubao Zhu
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Longxi Li
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Jinyu Zhao
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA
| | - Shaojie Tang
- School of Automation, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
| | - Jiaofen Nan
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Yanting Li
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Zhongqiang Zhao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial Hospital), Nanjing, 210029, China
| | - Jianzhou Shi
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial Hospital), Nanjing, 210029, China
| | - Zenghong Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial Hospital), Nanjing, 210029, China
| | - Chuang Han
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
| | - Zhixin Jiang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial Hospital), Nanjing, 210029, China.
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, Health Research Institute, Michigan Technological University, Houghton, MI, 49931, USA.
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4
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Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease. Med Sci (Basel) 2023; 11:medsci11010020. [PMID: 36976528 PMCID: PMC10053913 DOI: 10.3390/medsci11010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.
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Qiu J, Li L, Wang S, Zhang K, Chen Y, Yang S, Zhuang X. MyoPS-Net: Myocardial pathology segmentation with flexible combination of multi-sequence CMR images. Med Image Anal 2023; 84:102694. [PMID: 36495601 DOI: 10.1016/j.media.2022.102694] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/05/2022] [Accepted: 11/16/2022] [Indexed: 11/29/2022]
Abstract
Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application. The code has been released via https://github.com/QJYBall/MyoPS-Net.
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Affiliation(s)
- Junyi Qiu
- School of Data Science, Fudan University, Shanghai, China
| | - Lei Li
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Sihan Wang
- School of Data Science, Fudan University, Shanghai, China
| | - Ke Zhang
- School of Data Science, Fudan University, Shanghai, China
| | - Yinyin Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Medical Imaging, Shanghai Medical School, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Shan Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Medical Imaging, Shanghai Medical School, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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Ding Y, Xie W, Wong KKL, Liao Z. Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107041. [PMID: 35994871 DOI: 10.1016/j.cmpb.2022.107041] [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: 04/25/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI. METHODS Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed. RESULTS By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively. CONCLUSION This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.
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Affiliation(s)
- Yuhan Ding
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Weifang Xie
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Kelvin K L Wong
- School of Computer Science and Engineering, Central South University, Changsha 410000, China.
| | - Zhifang Liao
- School of Computer Science and Engineering, Central South University, Changsha 410000, China.
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7
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Lin M, Jiang M, Zhao M, Ukwatta E, White J, Chiu B. Cascaded triplanar autoencoder M-Net for fully automatic segmentation of left ventricle myocardial scar from three-dimensional late gadolinium-enhanced MR images. IEEE J Biomed Health Inform 2022; 26:2582-2593. [DOI: 10.1109/jbhi.2022.3146013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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8
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Wang KN, Yang X, Miao J, Li L, Yao J, Zhou P, Xue W, Zhou GQ, Zhuang X, Ni D. AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images. Med Image Anal 2022; 77:102362. [DOI: 10.1016/j.media.2022.102362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/26/2021] [Accepted: 01/10/2022] [Indexed: 10/19/2022]
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Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks. ALGORITHMS 2021. [DOI: 10.3390/a14080249] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.
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10
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Toupin S, Pezel T, Bustin A, Cochet H. Whole-Heart High-Resolution Late Gadolinium Enhancement: Techniques and Clinical Applications. J Magn Reson Imaging 2021; 55:967-987. [PMID: 34155715 PMCID: PMC9292698 DOI: 10.1002/jmri.27732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 12/15/2022] Open
Abstract
In cardiovascular magnetic resonance, late gadolinium enhancement (LGE) has become the cornerstone of myocardial tissue characterization. It is widely used in clinical routine to diagnose and characterize the myocardial tissue in a wide range of ischemic and nonischemic cardiomyopathies. The recent growing interest in imaging left atrial fibrosis has led to the development of novel whole‐heart high‐resolution late gadolinium enhancement (HR‐LGE) techniques. Indeed, conventional LGE is acquired in multiple breath‐holds with limited spatial resolution: ~1.4–1.8 mm in plane and 6–8 mm slice thickness, according to the Society for Cardiovascular Magnetic Resonance standardized guidelines. Such large voxel size prevents its use in thin structures such as the atrial or right ventricular walls. Whole‐heart 3D HR‐LGE images are acquired in free breathing to increase the spatial resolution (up to 1.3 × 1.3 × 1.3 mm3) and offer a better detection and depiction of focal atrial fibrosis. The downside of this increased resolution is the extended scan time of around 10 min, which hampers the spread of HR‐LGE in clinical practice. Initially introduced for atrial fibrosis imaging, HR‐LGE interest has evolved to be a tool to detect small scars in the ventricles and guide ablation procedures. Indeed, the detection of scars, nonvisible with conventional LGE, can be crucial in the diagnosis of myocardial infarction with nonobstructed coronary arteries, in the detection of the arrhythmogenic substrate triggering ventricular arrhythmia, and improve the confidence of clinicians in the challenging diagnoses such as the arrhythmogenic right ventricular cardiomyopathy. HR‐LGE also offers a precise visualization of left ventricular scar morphology that is particularly useful in planning ablation procedures and guiding them through the fusion of HR‐LGE images with electroanatomical mapping systems. In this narrative review, we attempt to summarize the technical particularities of whole‐heart HR‐LGE acquisition and provide an overview of its clinical applications with a particular focus on the ventricles.
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Affiliation(s)
- Solenn Toupin
- Siemens Healthcare France, Saint-Denis, France.,IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Université de Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Théo Pezel
- Division of Cardiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Cardiology, Lariboisiere Hospital, APHP, University of Paris, Paris, France
| | - Aurélien Bustin
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Université de Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Hubert Cochet
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Université de Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,Bordeaux University Hospital (CHU), Pessac, France
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11
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Shi X, Li C. Convexity preserving level set for left ventricle segmentation. Magn Reson Imaging 2021; 78:109-118. [PMID: 33592247 DOI: 10.1016/j.mri.2021.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/14/2021] [Accepted: 02/03/2021] [Indexed: 11/28/2022]
Abstract
In clinical applications of cardiac left ventricle (LV) segmentation, the segmented LV is desired to include the cavity, trabeculae, and papillary muscles, which form a convex shape. However, the intensities of trabeculae and papillary muscles are similar to myocardium. Consequently, segmentation algorithms may easily misclassify trabeculae and papillary muscles as myocardium. In this paper, we propose a level set method with a convexity preserving mechanism to ensure the convexity of the segmented LV. In the proposed level set method, the curvature of the level set contours is used to control their convexity, such that the level set contour is finally deformed as a convex shape. The experimental results and the comparison with other level set methods show the advantage of our method in terms of segmentation accuracy. Compared with the state-of-the-art methods using deep-learning, our method is able to achieve comparable segmentation accuracy without the need for training, while the deep-learning based method requires a large set of training data and high-quality manual segmentation. Therefore, our method can be conveniently used in situation where training data and their manual segmentation are not available.
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Affiliation(s)
- Xue Shi
- University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chunming Li
- University of Electronic Science and Technology of China, Chengdu 611731, China.
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12
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Ali RL, Qureshi NA, Liverani S, Roney CH, Kim S, Lim PB, Tweedy JH, Cantwell CD, Peters NS. Left Atrial Enhancement Correlates With Myocardial Conduction Velocity in Patients With Persistent Atrial Fibrillation. Front Physiol 2020; 11:570203. [PMID: 33304272 PMCID: PMC7693630 DOI: 10.3389/fphys.2020.570203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 10/16/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain. OBJECTIVE Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance. METHOD 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression. RESULTS An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: -0.11 m/s per unit increase in NI (P < 0.001) and -0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR. CONCLUSION At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.
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Affiliation(s)
- Rheeda L. Ali
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Norman A. Qureshi
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Silvia Liverani
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Caroline H. Roney
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Kim
- Abbot Medical, St. Paul, MN, United States
| | - P. Boon Lim
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Jennifer H. Tweedy
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Chris D. Cantwell
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- Department of Aeronautics, Imperial College London, London, United Kingdom
| | - Nicholas S. Peters
- ElectroCardioMaths Programme of The Imperial Centre for Cardiac Engineering, Imperial College London, London, United Kingdom
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
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13
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Singh Y, Shakyawar D, Hu W. Non-ischemic endocardial scar geometric remodeling toward topological machine learning. Proc Inst Mech Eng H 2020; 234:1029-1035. [DOI: 10.1177/0954411920937221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and better evaluation of cardiovascular diseases. Our goal is to apply topology data analysis toward machine learning algorithms to confirm the geometry of scar tissue, in addition to gaining better visualization and quantification of the scar tissue present. We have introduced architecture for integrating geometry in the form of topology toward machine learning. Morphological image processing was carried out to define the regions of the endocardial wall. We implemented convolutional neural networks on delayed enhancement cardiac computed tomography images for the recognition of scar tissue. Segmented two-dimensional images were stacked up to build the geometry of the scar area for visualization purposes. Mathematical calculations were executed for the validation of the scar tissue in addition to performing morphological image processing and marking the scar tissue present on the endocardial wall of the left ventricular. We applied convolutional neural network over convolution and pooling the layers with small sizes; we achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity, and found the dissimilarity distance between the normal endocardial tissue distances to be 9.37. This new concept in this study contributes toward a better understanding of scar structure and transmural variation of the endocardial wall of the left ventricular.
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Affiliation(s)
- Yashbir Singh
- Biomedical Engineering, Chung Yuan Christian University, Taoyuan
| | - Deepa Shakyawar
- Biomedical Engineering, Chung Yuan Christian University, Taoyuan
| | - Weichih Hu
- Biomedical Engineering, Chung Yuan Christian University, Taoyuan
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14
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Zabihollahy F, Rajan S, Ukwatta E. Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging. Curr Cardiol Rep 2020; 22:65. [PMID: 32562100 DOI: 10.1007/s11886-020-01321-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
PURPOSE OF REVIEW Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images. RECENT FINDINGS With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - S Rajan
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - E Ukwatta
- School of Engineering, University of Guelph, Guelph, ON, Canada
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Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review. Prog Cardiovasc Dis 2020; 63:367-376. [DOI: 10.1016/j.pcad.2020.03.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 03/08/2020] [Indexed: 02/06/2023]
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16
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Zabihollahy F, Rajchl M, White JA, Ukwatta E. Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi‐planar U‐Net (CMPU‐Net). Med Phys 2020; 47:1645-1655. [DOI: 10.1002/mp.14022] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 12/06/2019] [Accepted: 01/10/2020] [Indexed: 11/05/2022] Open
Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering Carleton University Ottawa ON Canada
| | - Martin Rajchl
- Department of Computing and Medicine Imperial College London London ON Canada
| | - James A. White
- Libin Cardiovascular Institute of Alberta University of Calgary Calgary AB Canada
| | - Eranga Ukwatta
- School of Engineering University of Guelph Guelph ON Canada
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17
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Abstract
OBJECTIVE. The purpose of this article is to review the nascent field of radiomics in cardiac MRI. CONCLUSION. Cardiac MRI produces a large number of images in a fairly inefficient manner with sometimes limited clinical application. In the era of precision medicine, there is increasing need for imaging to account for a broader array of diseases in an efficient and objective manner. Radiomics, the extraction and analysis of quantitative imaging features from medical imaging, may offer potential solutions to this need.
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Zhuang X. Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019; 41:2933-2946. [PMID: 30207950 DOI: 10.1109/tpami.2018.2869576] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The author proposes a method for simultaneous registration and segmentation of multi-source images, using the multivariate mixture model (MvMM) and maximum of log-likelihood (LL) framework. Specifically, the method is applied to the problem of myocardial segmentation combining the complementary information from multi-sequence (MS) cardiac magnetic resonance (CMR) images. For the image misalignment and incongruent data, the MvMM is formulated with transformations and is further generalized for dealing with the hetero-coverage multi-modality images (HC-MMIs). The segmentation of MvMM is performed in a virtual common space, to which all the images and misaligned slices are simultaneously registered. Furthermore, this common space can be divided into a number of sub-regions, each of which contains congruent data, thus the HC-MMIs can be modeled using a set of conventional MvMMs. Results show that MvMM obtained significantly better performance compared to the conventional approaches and demonstrated good potential for scar quantification as well as myocardial segmentation. The generalized MvMM has also demonstrated better robustness in the incongruent data, where some images may not fully cover the region of interest, and the full coverage can only be reconstructed combining the images from multiple sources.
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19
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Determination of Fumonisin B1 in maize using molecularly imprinted polymer nanoparticles-based assay. Food Chem 2019; 298:125044. [DOI: 10.1016/j.foodchem.2019.125044] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 06/16/2019] [Accepted: 06/18/2019] [Indexed: 11/20/2022]
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20
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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21
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Zabihollahy F, White JA, Ukwatta E. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images. Med Phys 2019; 46:1740-1751. [PMID: 30734937 DOI: 10.1002/mp.13436] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/10/2019] [Accepted: 01/31/2019] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. METHODS Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. RESULTS Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% ± 2.6% and 88.13% ± 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. CONCLUSIONS Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - James A White
- Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, USA
| | - Eranga Ukwatta
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
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22
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Abstract
The mechanical properties of soft tissues are closely associated with a variety of diseases. This motivates the development of elastography techniques in which tissue mechanical properties are quantitatively estimated through imaging. Magnetic resonance elastography (MRE) is a noninvasive phase-contrast MR technique wherein shear modulus of soft tissue can be spatially and temporally estimated. MRE has recently received significant attention due to its capability in noninvasively estimating tissue mechanical properties, which can offer considerable diagnostic potential. In this work, recent technology advances of MRE, its future clinical applications, and the related limitations will be discussed.
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Affiliation(s)
- Huiming Dong
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Richard D. White
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Internal Medicine-Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Arunark Kolipaka
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
- Department of Internal Medicine-Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
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23
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Leong CO, Lim E, Tan LK, Abdul Aziz YF, Sridhar GS, Socrates D, Chee KH, Lee Z, Liew YM. Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model. Magn Reson Med 2018; 81:1385-1398. [DOI: 10.1002/mrm.27486] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 06/30/2018] [Accepted: 07/15/2018] [Indexed: 01/18/2023]
Affiliation(s)
- Chen Onn Leong
- Department of Biomedical Engineering, Faculty of Engineering University of Malaya Kuala Lumpur Malaysia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering University of Malaya Kuala Lumpur Malaysia
| | - Li Kuo Tan
- Department of Biomedical Imaging, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia
- University Malaya Research Imaging Centre University of Malaya Kuala Lumpur Malaysia
| | - Yang Faridah Abdul Aziz
- Department of Biomedical Imaging, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia
- University Malaya Research Imaging Centre University of Malaya Kuala Lumpur Malaysia
| | | | - Dokos Socrates
- Department of Biomedical Engineering, Faculty of Engineering University of New South Wales Sydney NSW Australia
| | - Kok Han Chee
- Department of Medicine, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia
| | - Zhen‐Vin Lee
- Department of Medicine, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering University of Malaya Kuala Lumpur Malaysia
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Massalha S, Clarkin O, Thornhill R, Wells G, Chow BJW. Decision Support Tools, Systems, and Artificial Intelligence in Cardiac Imaging. Can J Cardiol 2018; 34:827-838. [PMID: 29960612 DOI: 10.1016/j.cjca.2018.04.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 04/25/2018] [Accepted: 04/26/2018] [Indexed: 12/22/2022] Open
Abstract
Noninvasive cardiac imaging is widely used for the diagnosis and management of cardiac patients. The increasing demand for cardiac imaging begins to exceed the number of available interpreting physicians, leaving less time to interpret studies. In addition, the busy clinician is facing the increasingly daunting task of keeping abreast of current medical advancements and the ongoing changes in disease diagnosis and therapy. Committing to memory and recalling such large volumes of information is challenging and is responsible for difficulties in adopting the rapid changes in imaging practice, and is likely partially responsible for errors in patient diagnosis and management. Diagnostic errors rank high in the cause of death in the United States, and are more common than any other medical error and are responsible for most malpractice claims. Most of these errors are related to cognitive errors. The use of artificial intelligence systems that can serve as complementary methods to assist humans with decision making can potentially prevent these errors. The past decades witnessed the development and integration of these tools, which can assist physicians with image interpretation. These tools work to optimize image quality for better visualization and accompany all imaging modalities, starting from patient selection for the appropriate test, patient preparation, image acquisition, processing, and finally interpretation. Current and future directions for technologies that support cardiac imaging physicians are discussed in this review.
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Affiliation(s)
- Samia Massalha
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Owen Clarkin
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Rebecca Thornhill
- Department of Radiology, University of Ottawa, Ottawa, Ontario, Canada
| | - Glenn Wells
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Benjamin J W Chow
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Ontario, Canada; Department of Radiology, University of Ottawa, Ottawa, Ontario, Canada.
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25
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Liu J, Zhuang X, Wu L, An D, Xu J, Peters T, Gu L. Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set. IEEE Trans Biomed Eng 2018; 64:2650-2661. [PMID: 28129147 DOI: 10.1109/tbme.2017.2657656] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.
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Affiliation(s)
- Jie Liu
- School of Biomedical EngineeringShanghai Jiao Tong University
| | | | - Lianming Wu
- Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine
| | - Dongaolei An
- Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine
| | - Jianrong Xu
- Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine
| | - Terry Peters
- Robarts Research InstituteUniversity of Western Ontario
| | - Lixu Gu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices 2017; 14:197-212. [PMID: 28277804 DOI: 10.1080/17434440.2017.1300057] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.
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Affiliation(s)
- Piotr J Slomka
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | - Damini Dey
- b Biomedical Imaging Research Institute , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | | | - Manish Motwani
- d Cardiovascular Imaging , Manchester Heart Centre, Manchester Royal Infirmary , Manchester , UK
| | - Daniel S Berman
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
| | - Guido Germano
- a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA
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Ukwatta E, Arevalo H, Li K, Yuan J, Qiu W, Malamas P, Wu KC, Trayanova NA, Vadakkumpadan F. Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1408-1419. [PMID: 26731693 PMCID: PMC4891256 DOI: 10.1109/tmi.2015.2512711] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.
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Affiliation(s)
- Eranga Ukwatta
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Correspondent author:
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kristina Li
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada
| | - Peter Malamas
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Fijoy Vadakkumpadan
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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28
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Ukwatta E, Arevalo H, Rajchl M, White J, Pashakhanloo F, Prakosa A, Herzka DA, McVeigh E, Lardo AC, Trayanova NA, Vadakkumpadan F. Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology. Med Phys 2016; 42:4579-90. [PMID: 26233186 DOI: 10.1118/1.4926428] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.
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Affiliation(s)
- Eranga Ukwatta
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Hermenegild Arevalo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Martin Rajchl
- Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
| | - James White
- Stephenson Cardiovascular MR Centre, University of Calgary, Calgary, Alberta T2N 2T9, Canada
| | - Farhad Pashakhanloo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Adityo Prakosa
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Daniel A Herzka
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Albert C Lardo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 and Division of Cardiology, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21224
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205; and Department of Biomedical Engineering, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21205
| | - Fijoy Vadakkumpadan
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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Karim R, Bhagirath P, Claus P, James Housden R, Chen Z, Karimaghaloo Z, Sohn HM, Lara Rodríguez L, Vera S, Albà X, Hennemuth A, Peitgen HO, Arbel T, Gonzàlez Ballester MA, Frangi AF, Götte M, Razavi R, Schaeffter T, Rhode K. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images. Med Image Anal 2016; 30:95-107. [PMID: 26891066 DOI: 10.1016/j.media.2016.01.004] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 11/12/2015] [Accepted: 01/15/2016] [Indexed: 11/17/2022]
Abstract
Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges.
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Affiliation(s)
- Rashed Karim
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
| | - Pranav Bhagirath
- Department of Cardiology, Haga Teaching Hospital, The Netherlands
| | - Piet Claus
- Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, Universiteit Leuven, Belgium
| | - R James Housden
- Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, Universiteit Leuven, Belgium
| | - Zhong Chen
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK
| | | | - Hyon-Mok Sohn
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK
| | | | | | - Xènia Albà
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anja Hennemuth
- Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Germany
| | - Heinz-Otto Peitgen
- Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Germany
| | - Tal Arbel
- The Centre for Intelligence Machines, McGill University, Canada
| | | | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic & Electrical Engineering, University of Sheffield, Sheffield, UK
| | - Marco Götte
- Department of Cardiology, Haga Teaching Hospital, The Netherlands
| | - Reza Razavi
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK
| | - Tobias Schaeffter
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK
| | - Kawal Rhode
- Department of Imaging Sciences & Biomedical Engineering, King's College London, UK
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30
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Giannakidis A, Nyktari E, Keegan J, Pierce I, Suman Horduna I, Haldar S, Pennell DJ, Mohiaddin R, Wong T, Firmin DN. Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation. Biomed Eng Online 2015; 14:88. [PMID: 26445883 PMCID: PMC4596471 DOI: 10.1186/s12938-015-0083-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 09/21/2015] [Indexed: 01/11/2023] Open
Abstract
Background Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Methods Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Results Global normalized intensity threshold levels TPRE = 1 1/4 and TPOST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. Conclusions The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies.
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Affiliation(s)
- Archontis Giannakidis
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK. .,National Heart and Lung Institute, Imperial College London, London, UK.
| | - Eva Nyktari
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK.
| | - Jennifer Keegan
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK. .,National Heart and Lung Institute, Imperial College London, London, UK.
| | - Iain Pierce
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK. .,National Heart and Lung Institute, Imperial College London, London, UK.
| | - Irina Suman Horduna
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK.
| | - Shouvik Haldar
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK.
| | - Dudley J Pennell
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK. .,National Heart and Lung Institute, Imperial College London, London, UK.
| | - Raad Mohiaddin
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK.
| | - Tom Wong
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK.
| | - David N Firmin
- Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK. .,National Heart and Lung Institute, Imperial College London, London, UK.
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Kalra K, Wang Q, McIver BV, Shi W, Guyton RA, Sun W, Sarin EL, Thourani VH, Padala M. Temporal changes in interpapillary muscle dynamics as an active indicator of mitral valve and left ventricular interaction in ischemic mitral regurgitation. J Am Coll Cardiol 2014; 64:1867-79. [PMID: 25444139 DOI: 10.1016/j.jacc.2014.07.988] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Revised: 06/26/2014] [Accepted: 07/29/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND Regional subpapillary myocardial hypokinesis may impair lateral reduction in the interpapillary muscle distance (IPMD) from diastole to systole, and adversely affect mitral valve geometry and tethering. OBJECTIVES The goal of this study was to investigate the impact of impaired lateral shortening in the interpapillary muscle distance on mitral valve geometry and function in ischemic heart disease. METHODS To quantify ventricular size/shape, regional myocardial contraction, lateral shortening of the IPMD, mitral valve geometry, and severity of mitral regurgitation, 67 patients with ischemic heart disease underwent cardiac magnetic resonance imaging, and a correlation analysis of measured parameters was performed. The impact of reduced IPMD shortening on mitral valve (dys)function was confirmed in swine and in a physiological computational mitral valve model. RESULTS Lateral shortening of the IPMD from diastole to systole was severely reduced in patients with moderate/severe ischemic mitral regurgitation (9.6 ± 2.8 mm), but preserved in mild IMR (11.5 ± 3.4 mm). Left ventricular size and ejection fraction did not differ between the groups. In swine with subpapillary infarction and impaired IPMD, mitral regurgitation was evident within 1 week, compared to those pigs with a nonpapillary infarction and preserved IPMD. In the controlled computational valve model, IPMD had the maximal impact on regurgitation, and was exacerbated with additional annular dilation. CONCLUSIONS By using cardiac magnetic resonance imaging in humans, we demonstrated that it is the impairment of lateral shortening between the papillary muscles, and not passive ventricular size, that governs the severity of mitral regurgitation. Loss of lateral shortening of IPMD tethers the leaflet edges and impairs their systolic closure, resulting in mitral regurgitation, even in small ventricles. Understanding the lateral dynamics of ventricular-valve interactions could aid the development of new repair techniques for ischemic mitral regurgitation.
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Affiliation(s)
- Kanika Kalra
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Qian Wang
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Bryant V McIver
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Weiwei Shi
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Robert A Guyton
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Wei Sun
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Eric L Sarin
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Vinod H Thourani
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia
| | - Muralidhar Padala
- Structural Heart Disease Research and Innovation Laboratory, Division of Cardiothoracic Surgery, Carlyle Fraser Heart Center, Emory University, Atlanta, Georgia.
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Karim R, Arujuna A, Housden RJ, Gill J, Cliffe H, Matharu K, Gill J, Rindaldi CA, O'Neill M, Rueckert D, Razavi R, Schaeffter T, Rhode K. A Method to Standardize Quantification of Left Atrial Scar From Delayed-Enhancement MR Images. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2014; 2:1800615. [PMID: 27170868 PMCID: PMC4861547 DOI: 10.1109/jtehm.2014.2312191] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Revised: 02/06/2014] [Accepted: 03/03/2014] [Indexed: 12/16/2022]
Abstract
Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for detecting left atrial (LA) fibrosis both pre and postradiofrequency ablation for the treatment of atrial fibrillation. Fixed thresholding models are frequently utilized clinically to segment and quantify scar in DE-MRI due to their simplicity. These methods fail to provide a standardized quantification due to interobserver variability. Quantification of scar can be used as an endpoint in clinical studies and therefore standardization is important. In this paper, we propose a segmentation algorithm for LA fibrosis quantification and investigate its performance. The algorithm was validated using numerical phantoms and 15 clinical data sets from patients undergoing LA ablation. We demonstrate that the approach produces good concordance with expert manual delineations. The method offers a standardized quantification technique for evaluation and interpretation of DE-MRI scans.
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Mahapatra D. Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. J Digit Imaging 2014; 26:721-30. [PMID: 23319109 DOI: 10.1007/s10278-012-9548-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology (ETH), CAB F 61.1, Universitätstrasse 6, 8092 Zurich, Switzerland.
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34
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Machann W, Geier O, Koeppe S, O’Donnell T, Greiser A, Breunig F, Sandstede J, Hahn D, Koestler H, Beer M. Reproducibility of manual and semi-automated late enhancement quantification in patients with Fabry disease. Acta Radiol 2014; 55:155-60. [PMID: 24078459 DOI: 10.1177/0284185113505275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Late enhancement (LE) imaging is increasingly used for diagnosis of non-ischemic cardiomyopathy. However, the mostly patchy appearance of LE in this context may reduce the reproducibility of LE measurement. PURPOSE To report intra- and inter-observer variabilities of LE measurements in Fabry disease using manual and semi-automated quantification. MATERIAL AND METHODS Twenty MRI data-sets of male patients aged 44 ± 7 years were analyzed twice (interval 12 months) by one observer and additionally once by a second observer. Left ventricular (LV) parameters were determined using cine MRI. Gradient-echo LE images were analyzed by manual planimetry and by a semi-automatic prototype software. Variabilities were determined by Bland-Altman analyses and additionally intra-class correlation coefficient (ICC) values were calculated to survey intra- and inter-observer reproducibility. RESULTS The amount of LE was 5.2 ± 5.1 mL or 2.8 ± 2.6 % of LV mass (observer 2). LE was detected predominantly intramurally in a patchy pattern. All patients had LE restricted to the basal infero-lateral parts of the LV. The extent of LE correlated to LV mass (207 ± 70 g, P < 0.05, r = 0.6). The intra- and inter-observer variabilities were -0.6 to 1.0 mL and -0.7 to 1.6 mL, respectively (95% confidence intervals). ICC values were 0.981-0.999. The semi-automatic software allowed quantification of LE areas in all patients. The comparison of LE amount determined by semi-automatic software versus manual planimetry yielded an intra-observer variability ranging from -1.9 to 2.3 mL. CONCLUSION Semi-automatic planimetry of patchy LE in patients with Fabry disease is feasible. The determined intra- and inter-observer variabilities for manual and semi-automatic planimetry were in the range of 20-40% of LE amount with high ICC values.
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Affiliation(s)
- Wolfram Machann
- Institute of Radiology, University of Würzburg, Würzburg, Germany
| | - Oliver Geier
- The Intervention Centre, Oslo University Hospital, Norway
| | - Sabrina Koeppe
- Institute of Radiology, University of Würzburg, Würzburg, Germany
| | | | | | - Frank Breunig
- Department of Internal Medicine, University of Würzburg, Würzburg, Germany
| | - Joern Sandstede
- Institute of Radiology, University of Würzburg, Würzburg, Germany
| | - Dietbert Hahn
- Institute of Radiology, University of Würzburg, Würzburg, Germany
| | - Herbert Koestler
- Institute of Radiology, University of Würzburg, Würzburg, Germany
- Comprehensive Heart Failure Center, University of Würzburg, Würzburg, Germany
| | - Meinrad Beer
- Institute of Radiology, University of Würzburg, Würzburg, Germany
- Comprehensive Heart Failure Center, University of Würzburg, Würzburg, Germany
- Department of Radiology, Medical University Graz, Graz, Austria
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35
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Rajchl M, Yuan J, White JA, Ukwatta E, Stirrat J, Nambakhsh CMS, Li FP, Peters TM. Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:159-172. [PMID: 24107924 DOI: 10.1109/tmi.2013.2282932] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.
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Karim R, Housden RJ, Balasubramaniam M, Chen Z, Perry D, Uddin A, Al-Beyatti Y, Palkhi E, Acheampong P, Obom S, Hennemuth A, Lu Y, Bai W, Shi W, Gao Y, Peitgen HO, Radau P, Razavi R, Tannenbaum A, Rueckert D, Cates J, Schaeffter T, Peters D, MacLeod R, Rhode K. Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. J Cardiovasc Magn Reson 2013; 15:105. [PMID: 24359544 PMCID: PMC3878126 DOI: 10.1186/1532-429x-15-105] [Citation(s) in RCA: 107] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 12/10/2013] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. METHODS The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. RESULTS Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. CONCLUSIONS The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.
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Affiliation(s)
- Rashed Karim
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - R James Housden
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | | | - Zhong Chen
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Daniel Perry
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA
| | - Ayesha Uddin
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Yosra Al-Beyatti
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Ebrahim Palkhi
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Prince Acheampong
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Samantha Obom
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Anja Hennemuth
- Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany
| | - YingLi Lu
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, UK
| | - Wenzhe Shi
- Department of Computing, Imperial College London, London, UK
| | - Yi Gao
- Psychiatry Neuroimaging Lab, Harvard Medical School, Boston, USA
| | - Heinz-Otto Peitgen
- Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany
| | - Perry Radau
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Reza Razavi
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Allen Tannenbaum
- School of Electrical and Computer Engineering, Boston University, Boston, USA
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Josh Cates
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA
| | - Tobias Schaeffter
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
| | - Dana Peters
- Magnetic Resonance Research Centre, Yale School of Medicine, Yale University, New Haven, USA
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
| | - Rob MacLeod
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA
| | - Kawal Rhode
- Department of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK
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Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information. Med Image Anal 2013; 17:685-97. [PMID: 23562069 DOI: 10.1016/j.media.2013.03.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2012] [Revised: 02/26/2013] [Accepted: 03/02/2013] [Indexed: 11/22/2022]
Abstract
Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and four sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.
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38
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Wei D, Sun Y, Ong SH, Chai P, Teo LL, Low AF. A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images. IEEE Trans Biomed Eng 2013; 60:1499-508. [PMID: 23362243 DOI: 10.1109/tbme.2013.2237907] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities with respect to normal myocardium. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts within the segmented myocardium. However, automatic segmentation is difficult usually due to the intensity heterogeneity of the myocardium and intensity similarity between the infarcts and blood pool. Besides, the slices of an LGE CMR dataset often suffer from spatial and intensity distortions, causing further difficulties in segmentation and classification. In this paper, we present a comprehensive 3-D framework for automatic quantification of LGE CMR images. In this framework, myocardium is segmented with a novel method that deforms coupled endocardial and epicardial meshes and combines information in both short- and long-axis slices, while infarcts are classified with a graph-cut algorithm incorporating intensity and spatial information. Moreover, both spatial and intensity distortions are effectively corrected with specially designed countermeasures. Experiments with 20 sets of real patient data show visually good segmentation and classification results that are quantitatively in strong agreement with those manually obtained by experts.
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Affiliation(s)
- Dong Wei
- Department of Electrical and Computer Engineering, National University of Singapore, 117576 Singapore.
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Hennemuth A, Friman O, Huellebrand M, Peitgen HO. Mixture-Model-Based Segmentation of Myocardial Delayed Enhancement MRI. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES 2013. [DOI: 10.1007/978-3-642-36961-2_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Gruszczynska K, Kirschbaum S, Baks T, Moelker A, Duncker DJ, Rossi A, Baron J, de Feyter PJ, Krestin GP, van Geuns RJM. Different algorithms for quantitative analysis of myocardial infarction with DE MRI: comparison with autopsy specimen measurements. Acad Radiol 2011; 18:1529-36. [PMID: 22055796 DOI: 10.1016/j.acra.2011.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2010] [Revised: 08/08/2011] [Accepted: 08/09/2011] [Indexed: 11/15/2022]
Abstract
RATIONALE AND OBJECTIVES To compare two semiautomated methods for measurement of infarcted myocardium area on delayed contrast enhanced magnetic resonance imaging, with histopathology findings as standard of reference. MATERIALS AND METHODS Percentage area of myocardial infarction was measured in 10 Yorkshire landrace pigs manually and using two semiautomated methods. The first (standard deviation method) used two operator-selected regions of interest (ROIs) and nine different cutoff values (one to nine times the standard deviation of signal intensity in normal myocardium) to identify infarction. The second (threshold method) used threshold values based on percentages of maximum signal intensity to identify infarction. Results were compared with histopathology findings. RESULTS Difference between percentage area of infarction obtained with standard deviation method and autopsy specimens was in the range: -13.5% to +13.2%. With threshold method (thresholds from 30% to 90% of signal intensity), difference was -15% to +23%. Manual contouring underestimated infarcted area by 2% comparing to autopsy results. The best agreement between histopathology and semi-automated software was achieved for 4 standard deviations with standard deviation method: difference -0.45%, and for a percentage threshold of 70% (difference +0.67%) with threshold method. However, with standard deviation method, there was statistically significant difference between ROIs based on their location in viable myocardium: mean difference 1.7 ± 4%, P < .0001. CONCLUSION Semiautomated measurement of myocardial infarcted area on delayed enhanced magnetic resonance images performs well compared to autopsy. The threshold method, based on percentages of maximum signal intensity is preferable over standard deviation method, which is more susceptible to variability from location of ROIs within viable myocardium.
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Affiliation(s)
- Katarzyna Gruszczynska
- Department of Radiology, Erasmus MC, Universitair Medisch Centrum, Thoraxcenter, Ba 585, 's-Gravendijkwal 230, 3015 CE Rotterdam, the Netherlands
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Tao Q, Milles J, Zeppenfeld K, Lamb HJ, Bax JJ, Reiber JHC, van der Geest RJ. Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information. Magn Reson Med 2011; 64:586-94. [PMID: 20665801 DOI: 10.1002/mrm.22422] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate assessment of the size and distribution of a myocardial infarction (MI) from late gadolinium enhancement (LGE) MRI is of significant prognostic value for postinfarction patients. In this paper, an automatic MI identification method combining both intensity and spatial information is presented in a clear framework of (i) initialization, (ii) false acceptance removal, and (iii) false rejection removal. The method was validated on LGE MR images of 20 chronic postinfarction patients, using manually traced MI contours from two independent observers as reference. Good agreement was observed between automatic and manual MI identification. Validation results showed that the average Dice indices, which describe the percentage of overlap between two regions, were 0.83 +/- 0.07 and 0.79 +/- 0.08 between the automatic identification and the manual tracing from observer 1 and observer 2, and the errors in estimated infarct percentage were 0.0 +/- 1.9% and 3.8 +/- 4.7% compared with observer 1 and observer 2. The difference between the automatic method and manual tracing is in the order of interobserver variation. In conclusion, the developed automatic method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging.
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Affiliation(s)
- Qian Tao
- LKEB - Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
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The culprit lesion and its consequences: combined visualization of the coronary arteries and delayed myocardial enhancement in dual-source CT: a pilot study. Eur Radiol 2010; 20:2834-43. [DOI: 10.1007/s00330-010-1864-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2010] [Accepted: 05/04/2010] [Indexed: 01/10/2023]
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Berbari R, Kachenoura N, Frouin F, Herment A, Mousseaux E, Bloch I. An automated quantification of the transmural myocardial infarct extent using cardiac DE-MR images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2009:4403-6. [PMID: 19964362 DOI: 10.1109/iembs.2009.5333691] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Evaluating myocardial viability is an important prognostic factor in the follow-up of infarctions. Delayed Enhancement magnetic resonance (DE-MR) imaging allows precise delineation of the infarct transmural extent. Visual interpretation is the most commonly used method to assess the myocardial infarction (MI) transmural extent. This study proposes to automate the segmentation of the (DE) images prior to the estimation of the extent of infarcted tissue. Indeed the segmentation of the myocardium was performed using cine contraction images which present a high contrast between cavity and myocardium. After the segmentation, the segmental transmurality is estimated on a conventional five point scale. A head to head comparison was performed between visual and quantitative analysis of infarct transmurality on DE-MR imaging. Results on 921 sub-segments (9 patients) showed an absolute agreement of 80% and a relative agreement (with one point difference) of 97%.
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Affiliation(s)
- R Berbari
- INSERM U678, UPMC, F-75013 Paris France and Téécom ParisTech (ENST), CNRS UMR 5141, F-75013 Paris France.
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Knowles BR, Caulfield D, Cooklin M, Rinaldi CA, Gill J, Bostock J, Razavi R, Schaeffter T, Rhode KS. 3-D visualization of acute RF ablation lesions using MRI for the simultaneous determination of the patterns of necrosis and edema. IEEE Trans Biomed Eng 2010; 57:1467-75. [PMID: 20172807 DOI: 10.1109/tbme.2009.2038791] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Catheter ablation using RF energy is a common treatment for atrial arrhythmias. Although this treatment provides a potential cure, currently, there remains a high proportion of patients returning for repeat ablations. Electrophysiologists have little information to verify that a lesion has been created in the myocardium. Temporary electrical block can be created from edema, which will subside. MRI can visualize acute and chronic ablation lesions using delayed-enhancement techniques. However, the ablation patterns cannot be determined from 2-D images alone. Using the combination of T(2)-weighted and delayed-enhancement MRI, ablation lesions can be characterized in terms of necrosis and edema. A novel 3-D visualization technique is presented that projects the image intensity due the lesions onto a 3-D cardiac surface, allowing the complete, simultaneous visualization of the delayed-enhancement and T(2)-weighted ablation patterns. Results show successful visualization of ablation patterns in 18 patients, and an application of this technique is presented in which electroanatomical mapping systems can be validated by overlaying the acquired ablation points onto the cardiac surfaces and assessing the correlation with the lesion maps.
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Affiliation(s)
- Benjamin R Knowles
- King's College London British Heart Foundation Centre, Division of Imaging Sciences, National Institute for Health Research Biomedical Research Centre, Guy's and St. Thomas' National Health Service Foundation Trust, King's College London, London, SE1 7EH, UK.
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Quantification automatisée de la transmuralité de l’infarctus du myocarde sur des images de rehaussement tardif en IRM. Ing Rech Biomed 2009. [DOI: 10.1016/j.irbm.2009.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Kwon DH, Halley CM, Carrigan TP, Zysek V, Popovic ZB, Setser R, Schoenhagen P, Starling RC, Flamm SD, Desai MY. Extent of left ventricular scar predicts outcomes in ischemic cardiomyopathy patients with significantly reduced systolic function: a delayed hyperenhancement cardiac magnetic resonance study. JACC Cardiovasc Imaging 2009; 2:34-44. [PMID: 19356530 DOI: 10.1016/j.jcmg.2008.09.010] [Citation(s) in RCA: 221] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2008] [Revised: 08/29/2008] [Accepted: 09/09/2008] [Indexed: 01/03/2023]
Abstract
OBJECTIVES The objective of the study was to determine whether the extent of left ventricular scar, measured with delayed hyperenhancement cardiac magnetic resonance (DHE-CMR), predicts survival in patients with ischemic cardiomyopathy (ICM) and severely reduced left ventricular ejection fraction (LVEF). BACKGROUND Patients with ICM and reduced LVEF have poor survival. Such patients have a high myocardial scar burden. CMR is highly accurate in delineation of myocardial scar. METHODS We studied 349 patients (76% men) with severe ICM (>or=70% disease in >or=1 epicardial coronary, and mean LVEF of 24%) that underwent DHE-CMR (Siemens 1.5-T scanner, Erlangen, Germany), between 2003 and 2006. Scar (quantified as percentage of myocardium) was defined on DHE-MR images as an intensity >2 standard deviations above the viable myocardium. Transmurality score was semiquantitatively recorded in a 17-segment model as: 0 = no scar, 1 = 1% to 25% scar, 2 = 26% to 50%, 3 = 51% to 75%, and 4 = >75%. The LVEF, demographic data, risk factors, need for cardiac transplantation (CTx), and all-cause mortality were recorded. RESULTS The mean age and follow-up were 65 +/- 11 years and 2.6 +/- 1.2 years (median 2.4 years [1.1, 3.5]), respectively. There were 56 events (51 deaths and 5 CTx). Mean scar percentage and transmurality score were higher in patients with events versus those without (39 +/- 22 vs. 30 +/- 20, p = 0.003, and 9.7 +/- 5 vs. 7.8 +/- 5, p = 0.004). On Cox proportional hazard survival analysis, quantified scar was greater than the median (30% of total myocardium), and female gender predicted events (relative risk 1.75 [95% Confidence Interval: 1.02 to 3.03] and relative risk 1.83 [95% Confidence Interval: 1.06 to 3.16], respectively, both p = 0.03). CONCLUSIONS In patients with ICM and severely reduced LVEF, a greater extent of myocardial scar, delineated by DHE-CMR is associated with increased mortality or the need for cardiac transplantation, potentially aiding further risk-stratification.
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Affiliation(s)
- Deborah H Kwon
- Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio 44195, USA
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Kwon DH, Smedira NG, Rodriguez ER, Tan C, Setser R, Thamilarasan M, Lytle BW, Lever HM, Desai MY. Cardiac Magnetic Resonance Detection of Myocardial Scarring in Hypertrophic Cardiomyopathy. J Am Coll Cardiol 2009; 54:242-9. [DOI: 10.1016/j.jacc.2009.04.026] [Citation(s) in RCA: 193] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2008] [Revised: 02/23/2009] [Accepted: 04/03/2009] [Indexed: 11/16/2022]
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Association Between Regional Ventricular Function and Myocardial Fibrosis in Hypertrophic Cardiomyopathy Assessed by Speckle Tracking Echocardiography and Delayed Hyperenhancement Magnetic Resonance Imaging. J Am Soc Echocardiogr 2008; 21:1299-305. [DOI: 10.1016/j.echo.2008.09.011] [Citation(s) in RCA: 182] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2008] [Indexed: 02/07/2023]
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Hennemuth A, Seeger A, Friman O, Miller S, Klumpp B, Oeltze S, Peitgen HO. A comprehensive approach to the analysis of contrast enhanced cardiac MR images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2008; 27:1592-1610. [PMID: 18955175 DOI: 10.1109/tmi.2008.2006512] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Current magnetic resonance imaging (MRI) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. Joint inspection of these three aspects yields valuable information for therapy planning, e.g., through classification of myocardium into healthy tissue, regions showing a reversible hypoperfusion, and infarction with additional information on the corresponding supplying artery. Standard imaging protocols normally provide image data with different orientations, resolutions and coverages for each of the three aspects, which makes a direct comparison of analysis results difficult. The purpose of this work is to develop methods for the alignment and combined analysis of these images. The proposed approach is applied to 21 datasets of healthy and diseased patients from the clinical routine. The evaluation shows that, despite limitations due to typical MRI artifacts, combined inspection is feasible and can yield clinically useful information.
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Affiliation(s)
- Anja Hennemuth
- Center for Medical Image Computing, MeVis Research, 28359 Bremen, Germany
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Carlsson M, Arheden H, Higgins CB, Saeed M. Magnetic resonance imaging as a potential gold standard for infarct quantification. J Electrocardiol 2008; 41:614-20. [DOI: 10.1016/j.jelectrocard.2008.06.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2008] [Revised: 06/18/2008] [Accepted: 06/27/2008] [Indexed: 11/16/2022]
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