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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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Ribeiro MAO, Nunes FLS. Left ventricle segmentation combining deep learning and deformable models with anatomical constraints. J Biomed Inform 2023; 142:104366. [PMID: 37086958 DOI: 10.1016/j.jbi.2023.104366] [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: 12/22/2022] [Revised: 03/19/2023] [Accepted: 04/17/2023] [Indexed: 04/24/2023]
Abstract
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed, in which deep learning networks have obtained remarkable performance. However, one of the main limitations of these approaches is the production of segmentations that contain anatomical errors. To avoid this limitation, we propose a new fully-automatic left ventricle segmentation method combining deep learning and deformable models. We propose a new level set energy formulation that includes exam-specific information estimated from the deep learning segmentation and shape constraints. The method is part of a pipeline containing pre-processing steps and a failure correction post-processing step. Experiments were conducted with the Sunnybrook and ACDC public datasets, and a private dataset. Results suggest that the method is competitive, that it can produce anatomically consistent segmentations, has good generalization ability, and is often able to estimate biomarkers close to the expert.
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Affiliation(s)
- Matheus A O Ribeiro
- University of São Paulo, Rua Arlindo Bettio, 1000, Vila Guaraciaba, São Paulo, 01000-000, São Paulo, Brazil.
| | - Fátima L S Nunes
- University of São Paulo, Rua Arlindo Bettio, 1000, Vila Guaraciaba, São Paulo, 01000-000, São Paulo, Brazil.
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3
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MMNet: A multi-scale deep learning network for the left ventricular segmentation of cardiac MRI images. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02720-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Habijan M, Babin D, Galić I, Leventić H, Romić K, Velicki L, Pižurica A. Overview of the Whole Heart and Heart Chamber Segmentation Methods. Cardiovasc Eng Technol 2020; 11:725-747. [DOI: 10.1007/s13239-020-00494-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022]
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Cardenas R, Curiale AH, Mato G. Left ventricle segmentation using a Bayesian approach with distance dependent shape priors. Biomed Phys Eng Express 2020; 6:045013. [PMID: 33444274 DOI: 10.1088/2057-1976/ab9556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.
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Affiliation(s)
- Rodrigo Cardenas
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. Centro Atómico Bariloche, Av. Bustillo 9500, R8402AGP S. C. de Bariloche, Río Negro, Argentina
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Niu Y, Qin L, Wang X. Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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7
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Segmentation Integrating Watershed and Shape Priors Applied to Cardiac Delayed Enhancement MR Images. Ing Rech Biomed 2017. [DOI: 10.1016/j.irbm.2017.06.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Baracho SF, Pinheiro DJLL, Godoy CMGD, Coelho RC. A segmentation method for myocardial ischemia/infarction applicable in heart photos. Comput Biol Med 2017. [DOI: 10.1016/j.compbiomed.2017.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Du Y, Du D. Cardiac image segmentation using generalized polynomial chaos expansion and level set function. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:652-655. [PMID: 29059957 DOI: 10.1109/embc.2017.8036909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.
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Hong-Seng G, Sayuti KA, Karim AHA. Investigation of random walks knee cartilage segmentation model using inter-observer reproducibility: Data from the osteoarthritis initiative. Biomed Mater Eng 2017; 28:75-85. [PMID: 28372262 DOI: 10.3233/bme-171658] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Existing knee cartilage segmentation methods have reported several technical drawbacks. In essence, graph cuts remains highly susceptible to image noise despite extended research interest; active shape model is often constraint by the selection of training data while shortest path have demonstrated shortcut problem in the presence of weak boundary, which is a common problem in medical images. OBJECTIVES The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method. METHODS Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories. RESULTS A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15). CONCLUSION The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.
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Affiliation(s)
- Gan Hong-Seng
- Medical Engineering Technology Section, Universiti Kuala Lumpur, British Malaysian Institute, 53100 Gombak, Selangor, Malaysia
| | - Khairil Amir Sayuti
- Department of Radiology, School of Medical Science, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
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Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2016; 29:155-95. [PMID: 26811173 PMCID: PMC4830888 DOI: 10.1007/s10334-015-0521-4] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.
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Affiliation(s)
- Peng Peng
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | | | - Ali Gooya
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | - Ling Shao
- Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Steffen E Petersen
- Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
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12
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Alizadeh Sani Z, Shalbaf A, Behnam H, Shalbaf R. Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction. J Digit Imaging 2015; 28:91-8. [PMID: 25059548 DOI: 10.1007/s10278-014-9722-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.
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Affiliation(s)
- Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical & Research Center, Iran University of Medical Science, Tehran, Iran
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Mahapatra D. Automatic cardiac segmentation using semantic information from random forests. J Digit Imaging 2015; 27:794-804. [PMID: 24895064 DOI: 10.1007/s10278-014-9705-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology, CAB E65.1, Universitatstrasse 6, Zurich, 8092, Switzerland,
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Mahapatra D. Cardiac MRI segmentation using mutual context information from left and right ventricle. J Digit Imaging 2013; 26:898-908. [PMID: 23354341 PMCID: PMC3782609 DOI: 10.1007/s10278-013-9573-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty" that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations.
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Affiliation(s)
- Dwarikanath Mahapatra
- Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1 Universitätstrasse, 68092, Zurich, Switzerland,
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