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Li D, Peng Y, Sun J, Guo Y. A task-unified network with transformer and spatial-temporal convolution for left ventricular quantification. Sci Rep 2023; 13:13529. [PMID: 37598235 PMCID: PMC10439898 DOI: 10.1038/s41598-023-40841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 08/17/2023] [Indexed: 08/21/2023] Open
Abstract
Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial-temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial-temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.
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Affiliation(s)
- Dapeng Li
- Shandong University of Science and Technology, Qingdao, China
| | - Yanjun Peng
- Shandong University of Science and Technology, Qingdao, China.
- Shandong Province Key Laboratory of Wisdom Mining Information Technology, Qingdao, China.
| | - Jindong Sun
- Shandong University of Science and Technology, Qingdao, China
| | - Yanfei Guo
- Shandong University of Science and Technology, Qingdao, China
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2
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Yu C, Liu H, Zhang H. Distilling sub-space structure across views for cardiac indices estimation. Med Image Anal 2023; 85:102764. [PMID: 36791621 DOI: 10.1016/j.media.2023.102764] [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: 11/28/2021] [Revised: 01/20/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023]
Abstract
Cardiac indices estimation in multi-view images attracts great attention due to its capability for cardiac function assessment. However, the variation of the cardiac indices across views causes that most cardiac indices estimation methods can only be trained separately in each view, resulting in low data utilization. To solve this problem, we have proposed distilling the sub-space structure across views to explore the multi-view data fully for cardiac indices estimation. In particular, the sub-space structure is obtained via building a n×n covariance matrix to describe the correlation between the output dimensions of all views. Then, an alternate convex search algorithm is proposed to optimize the cross-view learning framework by which: (i) we train the model with regularization of sub-space structure in each view; (ii) we update the sub-space structure based on the learned parameters from all views. In the end, we have conducted a series of experiments to verify the effectiveness of our proposed framework. The model is trained on three views (short axis, 2-chamber view and 4-chamber view) with two modalities (magnetic resonance imaging and computed tomography). Compared to the state-of-the-art methods, our method has demonstrated superior performance on cardiac indices estimation tasks.
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Affiliation(s)
- Chengjin Yu
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Huafeng Liu
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
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3
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A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation. Med Biol Eng Comput 2023; 61:1507-1520. [PMID: 36773119 DOI: 10.1007/s11517-023-02798-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 01/31/2023] [Indexed: 02/12/2023]
Abstract
Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the diagnostic performance, a deep learning-based, fully automatic, and clinical-ready framework was developed. Two collaborating deep learning networks including a 3D U-Net for left ventricle segmentation and a CNN for anatomical landmarks detection were trained on 276 subjects. With our processing framework, the 17-segment left ventricular model was automatically generated conformed to the clinical standard. Myocardial blood flow computed by commercial software was extracted within each segment and visualized against the bull's eye plot. The performance was validated on another 45 subjects. Coronary angiography and invasive fractional flow reserve measurements were also performed in these patients to serve as the gold standard for myocardial ischemia diagnosis. As a result, the diagnostic accuracy for our method was 81.08%, much higher than that for commercially available CTP analysis software (56.75%), and our method demonstrated a higher consistency (Kappa coefficient 0.759 vs. 0.585). Besides, the average processing time of our method was much lower (30 ± 10.5 s/subject vs. over 30 min/subject). In conclusion, the proposed deep learning-based framework could be a promising tool for assisting CTP analysis.
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Zeng Y, Tsui PH, Pang K, Bin G, Li J, Lv K, Wu X, Wu S, Zhou Z. MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. ULTRASONICS 2023; 127:106855. [PMID: 36206610 DOI: 10.1016/j.ultras.2022.106855] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 09/03/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
The segmentation of cardiac chambers and the quantification of clinical functional metrics in dynamic echocardiography are the keys to the clinical diagnosis of heart disease. Identifying the end-diastolic frames (EDFs) and end-systolic frames (ESFs) and manually segmenting the left ventricle in the echocardiographic cardiac cycle before obtaining the left ventricular ejection fraction (LVEF) is a time-consuming and tedious task for clinicians. In this work, we proposed a deep learning-based fully automated echocardiographic analysis method. We proposed a multi-attention efficient feature fusion network (MAEF-Net) to automatically segment the left ventricle. Then, EDFs and ESFs in all cardiac cycles were automatically detected to compute LVEF. The MAEF-Net method used a multi-attention mechanism to guide the network to capture heartbeat features effectively, while suppressing noise, and incorporated deep supervision mechanism and spatial pyramid feature fusion to enhance feature extraction capabilities. The proposed method was validated on the public EchoNet-Dynamic dataset (n = 1226). The Dice similarity coefficient (DSC) of the left ventricular segmentation reached (93.10 ± 2.22)%, and the mean absolute error (MAE) of cardiac phase detection was (2.36 ± 2.23) frames. The MAE for predicting LVEF was 6.29 %. The proposed method was also validated on a private clinical dataset (n = 22). The DSC of the left ventricular segmentation reached (92.81 ± 2.85)%, and the MAE of cardiac phase detection was (2.25 ± 2.27) frames. The MAE for predicting LVEF was 5.91 %, and the Pearson correlation coefficient r reached 0.96. The proposed method may be used as a new method for automatic left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. Our code and trained models will be made available publicly at https://github.com/xiaojinmao-code/MAEF-Net.
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Affiliation(s)
- Yan Zeng
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan 333323, Taiwan; Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Kunjing Pang
- Department of Echocardiography, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Guangyu Bin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Jiehui Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Cardiac Surgery, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Ke Lv
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Xining Wu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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5
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Argentiero A, Muscogiuri G, Rabbat MG, Martini C, Soldato N, Basile P, Baggiano A, Mushtaq S, Fusini L, Mancini ME, Gaibazzi N, Santobuono VE, Sironi S, Pontone G, Guaricci AI. The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review. J Clin Med 2022; 11:jcm11102866. [PMID: 35628992 PMCID: PMC9147423 DOI: 10.3390/jcm11102866] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
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Affiliation(s)
- Adriana Argentiero
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, 20149 Milan, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60660, USA;
| | - Chiara Martini
- Radiologic Sciences, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Nicolò Soldato
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Paolo Basile
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Andrea Baggiano
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Saima Mushtaq
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Laura Fusini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Maria Elisabetta Mancini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Nicola Gaibazzi
- Department of Cardiology, Azienda Ospedaliero-Universitaria, 43126 Parma, Italy;
| | - Vincenzo Ezio Santobuono
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
| | - Gianluca Pontone
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
- Department of Emergency and Organ Transplantation, University of Bari, 70121 Bari, Italy
- Correspondence:
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6
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Xue W, Chen Z, Wang T, Li S, Ni D. Regional Cardiac Motion Scoring with Multi-scale Motion-based Spatial Attention. IEEE J Biomed Health Inform 2022; 26:3116-3126. [PMID: 35320110 DOI: 10.1109/jbhi.2022.3161666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Regional cardiac motion scoring aims to classify the motion status of each myocardium segment into one of the four categories (normal, hypokinetic, akinetic and dyskinetic) from multiple short-axis MR sequences. It is essential for prognosis and early diagnosis for various cardiac diseases. However, the complex motion procedure of myocardium and the invisible pattern differences pose great challenges, leading to low performance for automatic methods. Most existing work mitigate the task by differentiating the normal motion patterns from the abnormal ones, without fine-grained motion scoring. We propose an effective method for the task of cardiac motion scoring by connecting a bottom-up and another top-down branch with a novel motion-based spatial attention module in multi-scale space. Specifically, we use the convolution blocks for low-level feature extraction that acts as a bottom-up mechanism, and the task of optical flow for explicit motion extraction that acts as a top-down mechanism for high-level allocation of spatial attention. To this end, a newly designed Multi-scale Motion-based Spatial Attention (MMSA) mechanism is used as the pivot connecting the bottom-up part and the top-down part, and adaptively weight the low-level features according to the motion information. Experimental results on a newly constructed dataset of 1440 myocardium segments from 90 subjects demonstrate that the proposed MMSA can accurately analyze the regional myocardium motion, with accuracies of 79.3% for 4-way motion scoring, 89.0% for abnormality detection, and correlation of 0.943 for estimation of motion score index. This work has great potential for practical assessment of cardiac motion function.
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Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI. MATHEMATICS 2022. [DOI: 10.3390/math10040627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.
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Ciyamala Kushbu S, Inbamalar TM. Making Semi-Automatic Segmentation Method to be Automatic Using Deep Learning for Biventricular Segmentation. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Ventricular Segmentation or Delineation of Cardiac Magnetic Resonance Imaging (CMRI) is significant in obtaining the cardiac contractile function, which in turn is taken as input for diagnosing Cardio Vascular Diseases (CVD). Many automatic and semi-automatic methods were evolved to
meet the constraints of diagnosing CVDs. Among these, semi-automatic methods require user intervention for delineation of ventricles, which consumes time and leads to intra and inter-observability, as with manual delineation. Thus, the automatic method is suggested by most of the researchers
to address the above-stated problem. We proposed Saliency-based Active contour U-Net (SACU-Net) for automatic bi-ventricular segmentation which is found to surpass the existing highest developed methods regarding closeness to the gold standard. Three schemes are used by our proposed algorithm,
namely 1. Saliency Detection Scheme for Region of Interest (ROI) Localization to concentrate only on Object of Interest, 2. Drop-out embedded U-net for Initial Contour evolution that performs initial segmentation and 3. Local-Global-based Regional active Contour (LGRAC) to fine-tune and avoid
leaking, merging of ventricles during Delineation. We used three datasets namely Automatic Cardiac Diagnosing Challenge (ACDC) of MICCAI 2017, Right Ventricular Segmentation Challenge (RVSC) of MICCAI 2012, and Sunny Brook (SB) of MICCAI 2009 dataset to test the adaptability nature of our
algorithm over different scanner resolutions and protocols. 100 and 50 CMRI Images of ACDC were used for training and testing respectively which obtained average Dice Coefficient (DC) metric of 0.963, 0.934, and 0.948 for Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and
Right Ventricular Cavity (RVC) respectively. 32 and 16 CMRI Images of RVSC are used for preparing and experimenting respectively, which obtained an average DC metric of 0.95 for RVC.30 and 15 CMRI Images of SB are used for preparing and experimenting respectively, which obtained average DC
metric of 0.96 and 0.97 for LVC and LVM, respectively. Hausdorff Distance (HD) Metrics are also calculated to learn the distance of proposed delineated ventricles to reach the gold standard. The above resultant metrics show the robustness of our proposed SACU-Net in the segmentation of ventricles
of CMRI than previous methods.
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Affiliation(s)
- S. Ciyamala Kushbu
- Department of Information and Communication Engineering, Anna University, Chennai 25, Tamilnadu, India
| | - T. M. Inbamalar
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, Tiruvallur 601206, Tamilnadu, India
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9
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Abdulkareem M, Brahier MS, Zou F, Taylor A, Thomaides A, Bergquist PJ, Srichai MB, Lee AM, Vargas JD, Petersen SE. Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment. Front Cardiovasc Med 2022; 9:822269. [PMID: 35155637 PMCID: PMC8831539 DOI: 10.3389/fcvm.2022.822269] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/04/2022] [Indexed: 12/28/2022] Open
Abstract
Objectives Cardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values. Methods Using a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment. Results Overall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates. Conclusions We proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- *Correspondence: Musa Abdulkareem
| | - Mark S. Brahier
- Georgetown University School of Medicine, Washington, DC, United States
| | - Fengwei Zou
- Montefiore Medical Centre, Bronx, NY, United States
| | | | | | | | | | - Aaron M. Lee
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Jose D. Vargas
- Veterans Affairs Medical Center, Washington, DC, United States
- Georgetown University, Washington, DC, United States
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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10
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Improved Segmentation of Cardiac MRI using efficient Pre-Processing Technique. JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2022. [DOI: 10.4018/jitr.299932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Cardiac Magnetic Resonance Imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac Magnetic Resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation based Non local means method.Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.
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11
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Xue W, Li J, Hu Z, Kerfoot E, Clough J, Oksuz I, Xu H, Grau V, Guo F, Ng M, Li X, Li Q, Liu L, Ma J, Grinias E, Tziritas G, Yan W, Atehortúa A, Garreau M, Jang Y, Debus A, Ferrante E, Yang G, Hua T, Li S. Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data. IEEE J Biomed Health Inform 2021; 25:3541-3553. [PMID: 33684050 PMCID: PMC7611810 DOI: 10.1109/jbhi.2021.3064353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm 2 for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.
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Affiliation(s)
- Wufeng Xue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
| | - Jiahui Li
- Beijing University of Post and Telecommunication, Beijing, China
| | | | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - James Clough
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Hao Xu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Fumin Guo
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Matthew Ng
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Lihong Liu
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Jin Ma
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Elias Grinias
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Georgios Tziritas
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Wenjun Yan
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Angélica Atehortúa
- LTSI UMR 1099, F-35000 Rennes, France; Universidad Nacional de Colombia, Bogotá, Colombia
| | | | - Yeonggul Jang
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University
| | - Alejandro Debus
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Enzo Ferrante
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Guanyu Yang
- Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Southeast University, Nanjing, China; LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
| | - Tiancong Hua
- Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs), Southeast University, Nanjing, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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Vesal S, Gu M, Maier A, Ravikumar N. Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification. IEEE J Biomed Health Inform 2021; 25:2698-2709. [PMID: 33351771 DOI: 10.1109/jbhi.2020.3046449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this article, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm 2, 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences.
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Hussain MA, Hamarneh G, Garbi R. Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1555-1567. [PMID: 33606626 DOI: 10.1109/tmi.2021.3060465] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automatic kidney localization in volumetric medical images is a critical step that often precedes subsequent data processing and analysis. Most current approaches perform kidney localization via an intermediate classification or regression step. This paper proposes an integrated deep learning approach for (i) kidney localization in computed tomography scans and (ii) segmentation-free renal volume estimation. Our localization method uses a selection-convolutional neural network that approximates the kidney inferior-superior span along the axial direction. Cross-sectional (2D) slices from the estimated span are subsequently used in a combined sagittal-axial Mask-RCNN that detects the organ bounding boxes on the axial and sagittal slices, the combination of which produces a final 3D organ bounding box. Furthermore, we use a fully convolutional network to estimate the kidney volume that skips the segmentation procedure. We also present a mathematical expression to approximate the 'volume error' metric from the 'Sørensen-Dice coefficient.' We accessed 100 patients' CT scans from the Vancouver General Hospital records and obtained 210 patients' CT scans from the 2019 Kidney Tumor Segmentation Challenge database to validate our method. Our method produces a kidney boundary wall localization error of ~2.4mm and a mean volume estimation error of ~5%.
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Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography. ELECTRONICS 2021. [DOI: 10.3390/electronics10040495] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.
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Yu C, Gao Z, Zhang W, Yang G, Zhao S, Zhang H, Zhang Y, Li S. Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:493-506. [PMID: 32310804 DOI: 10.1109/tnnls.2020.2984955] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten-fold cross-validation of over 2900 cardiac MRI images. Then, the fine-tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.
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Sharma K, Alsadoon A, Prasad PWC, Al-Dala'in T, Nguyen TQV, Pham DTH. A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105751. [PMID: 32957061 DOI: 10.1016/j.cmpb.2020.105751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 09/05/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND AND AIM deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. METHODOLOGY the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. RESULTS four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. CONCLUSION the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.
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Affiliation(s)
- Kiran Sharma
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia.
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Thair Al-Dala'in
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Tran Quoc Vinh Nguyen
- The University of Da Nang - University of Science and Education, Faculty of Information Technology, Vietnam
| | - Duong Thu Hang Pham
- The University of Da Nang - University of Science and Education, Faculty of Information Technology, Vietnam
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Punithakumar K, Ben Ayed I, Soliman AS, Goela A, Islam A, Li S, Noga M. 3D Motion Estimation of Left Ventricular Dynamics Using MRI and Track-to-Track Fusion. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 8:1800209. [PMID: 32467779 PMCID: PMC7247756 DOI: 10.1109/jtehm.2020.2989390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 03/26/2020] [Accepted: 04/15/2020] [Indexed: 11/21/2022]
Abstract
Objective: This study investigates the estimation of three dimensional (3D) left ventricular (LV) motion using the fusion of different two dimensional (2D) cine magnetic resonance (CMR) sequences acquired during routine imaging sessions. Although standard clinical cine CMR data is inherently 2D, the actual underlying LV dynamics lies in 3D space and cannot be captured entirely using single 2D CMR image sequences. By utilizing the image information from various short-axis and long-axis image sequences, the proposed method intends to estimate the dynamic state vectors consisting of the position and velocity information of the myocardial borders in 3D space. Method: The proposed method comprises two main components: tracking myocardial points in 2D CMR sequences and fusion of multiple trajectories correspond to the tracked points. The tracking which yields the set of corresponding temporal points representing the myocardial points is performed using a diffeomorphic nonrigid image registration approach. The trajectories obtained from each cine CMR sequence is then fused with the corresponding trajectories from other CMR views using an unscented Kalman smoother (UKS) and a track-to-track fusion algorithm. Results: We evaluated the proposed method by comparing the results against CMR imaging with myocardial tagging. We report a quantitative performance analysis by projecting the state vector estimates we obtained onto 2D tagged CMR images acquired from the same subjects and comparing them against harmonic phase estimates. The proposed algorithm yielded a competitive performance with a mean root mean square error of 1.3±0.5 pixels (1.8±0.6 mm) evaluated over 118 image sequences acquired from 30 subjects. Conclusion: This study demonstrates that fusing the information from short and long-axis views of CMR improves the accuracy of cardiac tissue motion estimation. Clinical Impact: The proposed method demonstrates that the fusion of tissue tracking information from long and short-axis views improves the binary classification of the automated regional function assessment.
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Affiliation(s)
- Kumaradevan Punithakumar
- 1Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABT6G 2R3Canada.,2Servier Virtual Cardiac CentreMazankowski Alberta Heart InstituteEdmontonABT6G 2B7Canada.,3Department of Computing ScienceUniversity of AlbertaEdmontonABT6G 2R3Canada
| | - Ismail Ben Ayed
- 4École de Technologie Supérieure (ÉTS)MontrealQCH3C 1K3Canada
| | | | - Aashish Goela
- 6Department of Medical ImagingWestern UniversityLondonONN6A 3K7Canada
| | - Ali Islam
- 7St. Joseph's Health Care LondonLondonONN6A 4V2Canada
| | - Shuo Li
- 6Department of Medical ImagingWestern UniversityLondonONN6A 3K7Canada
| | - Michelle Noga
- 1Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABT6G 2R3Canada.,2Servier Virtual Cardiac CentreMazankowski Alberta Heart InstituteEdmontonABT6G 2B7Canada
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Thomford NE, Bope CD, Agamah FE, Dzobo K, Owusu Ateko R, Chimusa E, Mazandu GK, Ntumba SB, Dandara C, Wonkam A. Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology. ACTA ACUST UNITED AC 2020; 24:264-277. [DOI: 10.1089/omi.2019.0142] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Nicholas Ekow Thomford
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
| | - Christian Domilongo Bope
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Francis Edem Agamah
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Kevin Dzobo
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Division of Medical Biochemistry, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Richmond Owusu Ateko
- University of Ghana Medical School, Department of Chemical Pathology, University of Ghana, Accra, Ghana
| | - Emile Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston Kuzamunu Mazandu
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Simon Badibanga Ntumba
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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Li T, Wei B, Cong J, Hong Y, Li S. Direct estimation of left ventricular ejection fraction via a cardiac cycle feature learning architecture. Comput Biol Med 2020; 118:103659. [DOI: 10.1016/j.compbiomed.2020.103659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/11/2020] [Accepted: 02/11/2020] [Indexed: 12/28/2022]
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Martin-Isla C, Campello VM, Izquierdo C, Raisi-Estabragh Z, Baeßler B, Petersen SE, Lekadir K. Image-Based Cardiac Diagnosis With Machine Learning: A Review. Front Cardiovasc Med 2020; 7:1. [PMID: 32039241 PMCID: PMC6992607 DOI: 10.3389/fcvm.2020.00001] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/06/2020] [Indexed: 01/28/2023] Open
Abstract
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
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Affiliation(s)
- Carlos Martin-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Victor M Campello
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Cristian Izquierdo
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Bettina Baeßler
- Department of Diagnostic & Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
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Luo G, Dong S, Wang W, Wang K, Cao S, Tam C, Zhang H, Howey J, Ohorodnyk P, Li S. Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification. Med Image Anal 2020; 59:101591. [DOI: 10.1016/j.media.2019.101591] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 08/25/2019] [Accepted: 10/21/2019] [Indexed: 10/25/2022]
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Ge R, Yang G, Chen Y, Luo L, Feng C, Zhang H, Li S. PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks. Med Image Anal 2019; 58:101554. [DOI: 10.1016/j.media.2019.101554] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 05/15/2019] [Accepted: 09/04/2019] [Indexed: 11/16/2022]
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Muthulakshmi M, Kavitha G. Deep CNN with LM learning based myocardial ischemia detection in cardiac magnetic resonance images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:824-827. [PMID: 31946022 DOI: 10.1109/embc.2019.8856838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a chronic dysfunction caused by deterioration in cardiac physiology. It results in about 31% of mortality worldwide. Among CVDs, myocardial ischemia (MI) leads to restriction in blood supply to heart tissues. There is a need to develop an effective computer aided detection (CAD) system to reduce the fatality. In this work, an attempt is made to perform mass screening of myocardial ischemic subjects and left ventricle (LV) volume estimation from cardiac magnetic resonance (CMR) images using deep convolutional neural network (CNN) with Levenberg-Marquardt (LM) learning. LV volume measurement is an important predictor of myocardial ischemia. The CMR samples used in this analysis are obtained from Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 database. The results of the proposed model are compared with deep CNN based on gradient descent (GD) learning algorithm. The results show that deep CNN architecture with LM learning classifies ischemic subjects with high accuracy (86.39%) and sensitivity (90%). The LM learning based method gives an AUC of 0.93. The estimated LV volumes obtained from the trained network gives high correlation with the ground truth. Thus the results support that proposed framework of deep CNN architecture with LM learning can be used as an effective CAD system for diagnosis of cardiovascular disorders.
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voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES 2019. [DOI: 10.1007/978-3-030-11726-9_29] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Xu C, Xu L, Gao Z, Zhao S, Zhang H, Zhang Y, Du X, Zhao S, Ghista D, Liu H, Li S. Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Med Image Anal 2018; 50:82-94. [DOI: 10.1016/j.media.2018.09.001] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 08/25/2018] [Accepted: 09/05/2018] [Indexed: 11/28/2022]
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Full left ventricle quantification via deep multitask relationships learning. Med Image Anal 2018; 43:54-65. [DOI: 10.1016/j.media.2017.09.005] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 08/25/2017] [Accepted: 09/18/2017] [Indexed: 12/22/2022]
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28
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Benameur N, Arous Y, Ben Abdallah N, Kraiem T. The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review. INTERNATIONAL JOURNAL OF CARDIOVASCULAR PRACTICE 2017. [DOI: 10.21859/ijcp-030101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Luo G, Dong S, Wang K, Zuo W, Cao S, Zhang H. Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images. IEEE Trans Biomed Eng 2017; 65:1924-1934. [PMID: 29035205 DOI: 10.1109/tbme.2017.2762762] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task. METHODS In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks. We study the end-to-end LV volume prediction method in items of the data preprocessing, network structure, and multiview fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new network structure for end-to-end LV volume estimation. Third, we explore the representational capacity of different slices and propose a fusion strategy to improve the prediction accuracy. RESULTS The evaluation results show that the proposed method outperforms other state-of-the-art LV volume estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth ( ${\rm{EDV:R}}^{{\rm 2}}={\text{0.974}}$, ${\rm{RMSE\,}}= {\text{9.6}}{\rm{\,ml}}$; ${\rm{ESV:R}}^{{\rm 2}}={\text{0.976}}$, ${\rm{RMSE}}= {\text{7.1}}\,{\text{ml}}$; ${\rm{EF:R}}^{{\rm 2}} ={\text{0.828}}$, ${\rm{RMSE}}= {\text{4.71}}\% $). CONCLUSION Experimental results prove that the proposed method may be useful for the LV volume prediction task. SIGNIFICANCE The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.
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Xue W, Islam A, Bhaduri M, Li S. Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2057-2067. [PMID: 28574348 DOI: 10.1109/tmi.2017.2709251] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After the manual labeling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks, such as a deep convolution autoencoder for cardiac image representation, and a multiple output convolution neural network for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44 ± 0.71 mm) and areas of cavity and myocardium (204 ± 133 mm2). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%), and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.
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31
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Lu X, Yang R, Xie Q, Ou S, Zha Y, Wang D. Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images. Biomed Eng Online 2017; 16:39. [PMID: 28351368 PMCID: PMC5370472 DOI: 10.1186/s12938-017-0323-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 02/10/2017] [Indexed: 12/01/2022] Open
Abstract
Background Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. Methods In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. Results We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 5.0 \times 10^{ - 4} $$\end{document}p=5.0×10-4) for left ventricle myocardium and from 0.6307 to 0.6519 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 6.0 \times 10^{ - 4} $$\end{document}p=6.0×10-4) for right atrium. Conclusions The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.
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Affiliation(s)
- Xuesong Lu
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Rongqian Yang
- School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
| | - Qinlan Xie
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Shanxing Ou
- Radiology Department, Guangzhou General Hospital of Guangzhou Military Area Command, Guangzhou, 510010, People's Republic of China
| | - Yunfei Zha
- Department of Radiology, Remin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Defeng Wang
- Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. .,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
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Yang C, Wu W, Su Y, Zhang S. Left ventricle segmentation via two-layer level sets with circular shape constraint. Magn Reson Imaging 2017; 38:202-213. [PMID: 28108373 DOI: 10.1016/j.mri.2017.01.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 01/13/2017] [Accepted: 01/14/2017] [Indexed: 11/15/2022]
Abstract
This paper proposes a circular shape constraint and a novel two-layer level set method for the segmentation of the left ventricle (LV) from short-axis magnetic resonance images without training any shape models. Since the shape of LV throughout the apex-base axis is close to a ring shape, we propose a circle fitting term in the level set framework to detect the endocardium. The circle fitting term imposes a penalty on the evolving contour from its fitting circle, and thereby handles quite well with issues in LV segmentation, especially the presence of outflow track in basal slices and the intensity overlap between TPM and the myocardium. To extract the whole myocardium, the circle fitting term is incorporated into two-layer level set method. The endocardium and epicardium are respectively represented by two specified level contours of the level set function, which are evolved by an edge-based and a region-based active contour model. The proposed method has been quantitatively validated on the public data set from MICCAI 2009 challenge on the LV segmentation. Experimental results and comparisons with state-of-the-art demonstrate the accuracy and robustness of our method.
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Affiliation(s)
- Cong Yang
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
| | - Weiguo Wu
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
| | - Yuanqi Su
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, PR China
| | - Shaoxiang Zhang
- Digital Medicine Institute, Biomedical Engineering College, Third Military Medical University, Chongqing, PR China
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Zhang Y, Kwon D, Pohl KM. Computing group cardinality constraint solutions for logistic regression problems. Med Image Anal 2017; 35:58-69. [PMID: 27318592 PMCID: PMC5099121 DOI: 10.1016/j.media.2016.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 05/25/2016] [Accepted: 05/27/2016] [Indexed: 02/03/2023]
Abstract
We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints.
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Affiliation(s)
- Yong Zhang
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA 94304, USA.
| | - Dongjin Kwon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA 94304, USA; Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA 94304, USA; Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
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34
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Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation. Med Image Anal 2016; 30:120-129. [DOI: 10.1016/j.media.2015.07.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 03/29/2015] [Accepted: 07/11/2015] [Indexed: 12/19/2022]
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Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns. Int J Biomed Imaging 2016; 2016:4301087. [PMID: 27034648 PMCID: PMC4791492 DOI: 10.1155/2016/4301087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Revised: 02/05/2016] [Accepted: 02/11/2016] [Indexed: 01/19/2023] Open
Abstract
Purpose. To develop a method for identifying abnormal myocardial function based on studying the normalized wall motion pattern during the cardiac cycle. Methods. The temporal pattern of the normalized myocardial wall thickness is used as a feature vector to assess the cardiac wall motion abnormality. Principal component analysis is used to reduce the feature dimensionality and the maximum likelihood method is used to differentiate between normal and abnormal features. The proposed method was applied on a dataset of 27 cases from normal subjects and patients. Results. The developed method achieved 81.5%, 85%, and 88.5% accuracy for identifying abnormal contractility in the basal, midventricular, and apical slices, respectively. Conclusions. A novel feature vector, namely, the normalized wall thickness, has been introduced for detecting myocardial regional wall motion abnormality. The proposed method provides assessment of the regional myocardial contractility for each cardiac segment and slice; therefore, it could be a valuable tool for automatic and fast determination of regional wall motion abnormality from conventional cine MRI images.
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Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2016; 29:155-95. [PMID: 26811173 PMCID: PMC4830888 DOI: 10.1007/s10334-015-0521-4] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.
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Affiliation(s)
- Peng Peng
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | | | - Ali Gooya
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | - Ling Shao
- Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Steffen E Petersen
- Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
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Punithakumar K, Ben Ayed I, Afshin M, Goela A, Islam A, Li S, Boulanger P, Becher H, Noga M. Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:58-66. [PMID: 26614019 DOI: 10.1016/j.cmpb.2015.10.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2015] [Revised: 09/21/2015] [Accepted: 10/20/2015] [Indexed: 06/05/2023]
Abstract
UNLABELLED Anatomical cine cardiovascular magnetic resonance (CMR) imaging is widely used to assess the systolic cardiac function because of its high soft tissue contrast. Assessment of diastolic LV function has not regularly been performed due the complex and time consuming procedures. This study presents a semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images. The proposed method is based on three main steps: (1) non-rigid registration, which yields a sequence of endocardial boundary points over the cardiac cycle based on a user-provided contour on the first frame; (2) LV volume and filling rate computations over the cardiac cycle; and (3) automated detection of the peak values of early (E) and late ventricular (A) filling waves. In 47 patients cine CMR imaging and Doppler-echocardiographic imaging were performed. CMR measurements of peak values of the E and A waves as well as the deceleration time were compared with the corresponding values obtained in Doppler-Echocardiography. For the E/A ratio the proposed algorithm for CMR yielded a Cohen's kappa measure of 0.70 and a Gwet's AC1 coefficient of 0.70. CONCLUSION Semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images provides mitral inflow measurements comparable to Doppler-Echocardiography.
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Affiliation(s)
- Kumaradevan Punithakumar
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
| | - Ismail Ben Ayed
- Ecole de Technologie Superieure (ETS), University of Quebec, Montreal, QC, Canada
| | - Mariam Afshin
- Dept. of Medical Imaging, Sunnybrook Health Science Centre, Toronto, ON, Canada
| | | | - Ali Islam
- St. Joseph's Health Care, London, ON, Canada
| | - Shuo Li
- Department of Medical Imaging and Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Pierre Boulanger
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Harald Becher
- Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Michelle Noga
- Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Liang X, Garnavi R, Wail S, Prasanna P. Automatic segmentation of the left ventricle into 17 anatomical regions in cardiac MR imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:6531-5. [PMID: 26737789 DOI: 10.1109/embc.2015.7319889] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Analysis and characterization of anatomical segments in the left ventricle (LV) of the heart in cardiac MRI convey clinical significance. Based on the standard defined by the American Heart Association (AHA), the LV is divided into 17 anatomical segments. In this paper, we propose a novel method to automatically partition the LV into 17 segments, which allows automated analysis of these segments. Our method starts with assigning each slice with a section tag by using the papillary muscles and the LV cavity as references: basal, mid-cavity, apical and apex. It then partitions each slice into 4 or 6 segments by extracting the relevant points on the outer circle of a fitted cylinder and identifying the image orientation by using the lung as a reference. We evaluate our method on 45 patients with different cardiac conditions. The partition of mid-cavity has the best agreement with the ground truth, followed by basal and then apical sections for all groups of patients.
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Zhang X, Ambale-Venkatesh B, Bluemke DA, Cowan BR, Finn JP, Kadish AH, Lee DC, Lima JAC, Hundley WG, Suinesiaputra A, Young AA, Medrano-Gracia P. Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. J Transl Med 2015; 13:343. [PMID: 26531126 PMCID: PMC4632345 DOI: 10.1186/s12967-015-0709-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 10/23/2015] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Although adverse left ventricular shape changes (remodeling) after myocardial infarction (MI) are predictive of morbidity and mortality, current clinical assessment is limited to simple mass and volume measures, or dimension ratios such as length to width ratio. We hypothesized that information maximizing component analysis (IMCA), a supervised feature extraction method, can provide more efficient and sensitive indices of overall remodeling. METHODS IMCA was compared to linear discriminant analysis (LDA), both supervised methods, to extract the most discriminatory global shape changes associated with remodeling after MI. Finite element shape models from 300 patients with myocardial infarction from the DETERMINE study (age 31-86, mean age 63, 20 % women) were compared with 1991 asymptomatic cases from the MESA study (age 44-84, mean age 62, 52 % women) available from the Cardiac Atlas Project. IMCA and LDA were each used to identify a single mode of global remodeling best discriminating the two groups. Logistic regression was employed to determine the association between the remodeling index and MI. Goodness-of-fit results were compared against a baseline logistic model comprising standard clinical indices. RESULTS A single IMCA mode simultaneously describing end-diastolic and end-systolic shapes achieved best results (lowest Deviance, Akaike information criterion and Bayesian information criterion, and the largest area under the receiver-operating-characteristic curve). This mode provided a continuous scale where remodeling can be quantified and visualized, showing that MI patients tend to present larger size and more spherical shape, more bulging of the apex, and thinner wall thickness. CONCLUSIONS IMCA enables better characterization of global remodeling than LDA, and can be used to quantify progression of disease and the effect of treatment. These data and results are available from the Cardiac Atlas Project ( http://www.cardiacatlas.org ).
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Affiliation(s)
- Xingyu Zhang
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Bharath Ambale-Venkatesh
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA.
| | - David A Bluemke
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA.
| | - Brett R Cowan
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - J Paul Finn
- Department of Radiology, UCLA, Los Angeles, USA.
| | - Alan H Kadish
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA.
| | - Daniel C Lee
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, USA.
| | - Joao A C Lima
- The Donald W. Reynolds Cardiovascular Clinical Research Center, The Johns Hopkins University, Baltimore, USA.
| | | | - Avan Suinesiaputra
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Alistair A Young
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
| | - Pau Medrano-Gracia
- Department of Anatomy with Radiology, Grafton Campus, University of Auckland, 85 Park Road, Grafton, Auckland, 1148, New Zealand.
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Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects. FUNCTIONAL IMAGING AND MODELING OF THE HEART 2015. [DOI: 10.1007/978-3-319-20309-6_1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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