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K G, N UM, R V, B GP. Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks. Technol Health Care 2024:THC240062. [PMID: 39058464 DOI: 10.3233/thc-240062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
BACKGROUND The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy. OBJECTIVE/METHODS This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets. RESULTS/CONCLUSIONS The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.
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Affiliation(s)
- Gayathri K
- Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
| | - Uma Maheswari N
- Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
| | - Venkatesh R
- Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
| | - Ganesh Prabu B
- Department of Electrical and Electronics Engineering, University College of Engineering Dindigul, Tamil Nadu, India
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Heerdt PM, Kheyfets VO, Oakland HT, Joseph P, Singh I. Right Ventricular Pressure Waveform Analysis-Clinical Relevance and Future Directions. J Cardiothorac Vasc Anesth 2024:S1053-0770(24)00395-1. [PMID: 39025682 DOI: 10.1053/j.jvca.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 06/02/2024] [Accepted: 06/15/2024] [Indexed: 07/20/2024]
Abstract
Continuous measurement of pressure in the right atrium and pulmonary artery has commonly been used to monitor right ventricular function in critically ill and surgical patients. This approach is largely based upon the assumption that right atrial and pulmonary arterial pressures provide accurate surrogates for diastolic filling and peak right ventricular pressures, respectively. However, due to both technical and physiologic factors, this assumption is not always true. Accordingly, recent studies have begun to emphasize the potential clinical value of also measuring right ventricular pressure at the bedside. This has highlighted both past and emerging research demonstrating the utility of analyzing not only the amplitude of right ventricular pressure but also the shape of the pressure waveform. This brief review summarizes data demonstrating that combining conventional measurements of right ventricular pressure with variables derived from waveform shape allows for more comprehensive and ideally continuous bedside assessment of right ventricular function, particularly when combined with stroke volume measurement or 3D echocardiography, and discusses the potential use of right ventricular pressure analysis in computational models for evaluating cardiac function.
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Affiliation(s)
- Paul M Heerdt
- Department of Anesthesiology, Applied Hemodynamics, Yale School of Medicine, New Haven, CT.
| | - Vitaly O Kheyfets
- Department of Pediatrics-Critical Care Medicine, University of Colorado - Anschutz Medical Campus, Denver, CO
| | - Hannah T Oakland
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
| | - Phillip Joseph
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
| | - Inderjit Singh
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
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3
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Baccouch W, Oueslati S, Solaiman B, Lahidheb D, Labidi S. Automatic left ventricle volume and mass quantification from 2D cine-MRI: Investigating papillary muscle influence. Med Eng Phys 2024; 127:104162. [PMID: 38692762 DOI: 10.1016/j.medengphy.2024.104162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/01/2024] [Accepted: 03/27/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE Early detection of cardiovascular diseases is based on accurate quantification of the left ventricle (LV) function parameters. In this paper, we propose a fully automatic framework for LV volume and mass quantification from 2D-cine MR images already segmented using U-Net. METHODS The general framework consists of three main steps: Data preparation including automatic LV localization using a convolution neural network (CNN) and application of morphological operations to exclude papillary muscles from the LV cavity. The second step consists in automatically extracting the LV contours using U-Net architecture. Finally, by integrating temporal information which is manifested by a spatial motion of myocytes as a third dimension, we calculated LV volume, LV ejection fraction (LVEF) and left ventricle mass (LVM). Based on these parameters, we detected and quantified cardiac contraction abnormalities using Python software. RESULTS CNN was trained with 35 patients and tested on 15 patients from the ACDC database with an accuracy of 99,15 %. U-Net architecture was trained using ACDC database and evaluated using local dataset with a Dice similarity coefficient (DSC) of 99,78 % and a Hausdorff Distance (HD) of 4.468 mm (p < 0,001). Quantification results showed a strong correlation with physiological measures with a Pearson correlation coefficient (PCC) of 0,991 for LV volume, 0.962 for LVEF, 0.98 for stroke volume (SV) and 0.923 for LVM after pillars' elimination. Clinically, our method allows regional and accurate identification of pathological myocardial segments and can serve as a diagnostic aid tool of cardiac contraction abnormalities. CONCLUSION Experimental results prove the usefulness of the proposed method for LV volume and function quantification and verify its potential clinical applicability.
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Affiliation(s)
- Wafa Baccouch
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia.
| | - Sameh Oueslati
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia
| | - Basel Solaiman
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, 29238, Brest Cedex, France
| | - Dhaker Lahidheb
- University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; Department of Cardiology, Military Hospital of Tunis, Tunis, Tunisia
| | - Salam Labidi
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia
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4
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Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Khosravi A, Ling SH, Delfan N, Zhang YD, Wang SH, Gorriz JM, Alinejad-Rokny H, Acharya UR. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review. Comput Biol Med 2023; 160:106998. [PMID: 37182422 DOI: 10.1016/j.compbiomed.2023.106998] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023]
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.
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Affiliation(s)
- Mahboobeh Jafari
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Afshin Shoeibi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Navid Ghassemi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Parisa Moridian
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia
| | - Niloufar Delfan
- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia; Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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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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Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, Tee YK, Dhanalakshmi S, Lai KW. An Overview of Deep Learning Methods for Left Ventricle Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4208231. [PMID: 36756163 PMCID: PMC9902166 DOI: 10.1155/2023/4208231] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/25/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023]
Abstract
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Raza Ali
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azira Khalil
- Faculty of Science & Technology, Universiti Sains Islam Malaysia, Nilai 71800, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
| | - Khin Wee Lai
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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7
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Li M, Chen Y, Mao Y, Jiang M, Liu Y, Zhan Y, Li X, Su C, Zhang G, Zhou X. Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:4186648. [PMID: 34795790 PMCID: PMC8594980 DOI: 10.1155/2021/4186648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/17/2021] [Accepted: 10/23/2021] [Indexed: 02/05/2023]
Abstract
Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI. However, it takes time for cardiologists to measure DCM-related parameters to decide whether patients have this disease. We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing DCM. In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac MR image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart. Machine learning classifiers use extracted parameters as input to predict normal people and patients with DCM, among which Random forest classifier gives the highest accuracy. The results show that the proposed system can be effectively utilized to detect and diagnose DCM automatically. The experimental results suggest the capabilities and advantages of the proposed method to diagnose DCM. A small amount of sample input can generate results comparable to more complex methods.
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Affiliation(s)
- Mingliang Li
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yidong Chen
- School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
| | - Yujie Mao
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Mingfeng Jiang
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yujun Liu
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yuefu Zhan
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
- Hainan Women and Children's Medical Center, Haikou, China
| | - Xiangying Li
- Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Caixia Su
- School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China
| | - Guangming Zhang
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
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Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021; 8:736223. [PMID: 34631834 PMCID: PMC8493089 DOI: 10.3389/fcvm.2021.736223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
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Affiliation(s)
- Riccardo Maragna
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Carlo Maria Giacari
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Marco Guglielmo
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Baggiano
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Laura Fusini
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari, Bari, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,Center for Molecular Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Mark Rabbat
- Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago, Chicago, IL, United States.,Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Gianluca Pontone
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
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Su YH, Jiang W, Chitrakar D, Huang K, Peng H, Hannaford B. Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation. SENSORS (BASEL, SWITZERLAND) 2021; 21:5163. [PMID: 34372398 PMCID: PMC8346972 DOI: 10.3390/s21155163] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/19/2022]
Abstract
Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.
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Affiliation(s)
- Yun-Hsuan Su
- Department of Computer Science, Mount Holyoke College, 50 College Street, South Hadley, MA 01075, USA;
| | - Wenfan Jiang
- Department of Computer Science, Mount Holyoke College, 50 College Street, South Hadley, MA 01075, USA;
| | - Digesh Chitrakar
- Department of Engineering, Trinity College, 300 Summit St., Hartford, CT 06106, USA; (D.C.); (K.H.)
| | - Kevin Huang
- Department of Engineering, Trinity College, 300 Summit St., Hartford, CT 06106, USA; (D.C.); (K.H.)
| | - Haonan Peng
- Department of Electrical and Computer Engineering, University of Washington, 185 Stevens Way, Paul Allen Center, Seattle, WA 98105, USA; (H.P.); (B.H.)
| | - Blake Hannaford
- Department of Electrical and Computer Engineering, University of Washington, 185 Stevens Way, Paul Allen Center, Seattle, WA 98105, USA; (H.P.); (B.H.)
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10
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Tang X, Peng J, Zhong B, Li J, Yan Z. Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 205:106110. [PMID: 33910149 DOI: 10.1016/j.cmpb.2021.106110] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 04/07/2021] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVE For medical image segmentation, deep learning-based methods have achieved state-of-the-art performance. However, the powerful spectral representation in the field of image processing is rarely considered in these models. METHODS In this work, we propose to introduce frequency representation into convolution neural networks (CNNs) and design a novel model, tKFC-Net, to combine powerful feature representation in both frequency and spatial domains. Through the Fast Fourier Transform (FFT) operation, frequency representation is employed on pooling, upsampling, and convolution without any adjustments to the network architecture. Furthermore, we replace original convolution with twin-Kernel Fourier Convolution (t-KFC), a new designed convolution layer, to specify the convolution kernels for particular functions and extract features from different frequency components. RESULTS We experimentally show that our method has an edge over other models in the task of medical image segmentation. Evaluated on four datasets-skin lesion segmentation (ISIC 2018), retinal blood vessel segmentation (DRIVE), lung segmentation (COVID-19-CT-Seg), and brain tumor segmentation (BraTS 2019), the proposed model achieves outstanding results: the metric F1-Score is 0.878 for ISIC 2018, 0.8185 for DRIVE, 0.9830 for COVID-19-CT-Seg, and 0.8457 for BraTS 2019. CONCLUSION The introduction of spectral representation retains spectral features which result in more accurate segmentation. The proposed method is orthogonal to other topology improvement methods and very convenient to be combined.
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Affiliation(s)
- Xianlun Tang
- Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Jiangping Peng
- Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Bing Zhong
- Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Jie Li
- College of Mobile Telecommunications, Chongqing University of Posts and Telecom, Chongqing 401520, China
| | - Zhenfu Yan
- Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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11
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Zhu X, Wei Y, Lu Y, Zhao M, Yang K, Wu S, Zhang H, Wong KKL. Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105914. [PMID: 33383330 DOI: 10.1016/j.cmpb.2020.105914] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 12/09/2020] [Indexed: 06/12/2023]
Abstract
In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks (CNNs) for segmentation. Medical experts determine the consistency between the proposed ML approach, which is a state-of-the-art deep learning method, and the manual segmentation approach. These methods are compared in terms of performance indicators such as the ventricular area (VA), ventricular maximum diameter (VMXD), ventricular minimum diameter (VMID), and ventricular long axis angle (AVLA) measurements. Furthermore, the Dice similarity coefficient, Jaccard index, and Hausdorff distance are measured to estimate the agreement of the LV segmented results between the automatic and visual approaches. The obtained results indicate that the proposed techniques for LV segmentation are useful and practical. There is no significant difference between the use of AC and CNN in image segmentation; however, the AC method could obtain comparable accuracy as the CNN method using less training data and less run-time.
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Affiliation(s)
- Xiliang Zhu
- Department of Cardiovascular Surgery, Henan Province People's Hospital, Fuwai Central China Cardiovascular Hospital, Henan Cardiovascular Hospital and Zhengzhou University, Zhengzhou, China
| | - Yang Wei
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yu Lu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
| | - Ming Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Ke Yang
- School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, China
| | - Shiqian Wu
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.
| | - Hui Zhang
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Kelvin K L Wong
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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Kawel-Boehm N, Hetzel SJ, Ambale-Venkatesh B, Captur G, Francois CJ, Jerosch-Herold M, Salerno M, Teague SD, Valsangiacomo-Buechel E, van der Geest RJ, Bluemke DA. Reference ranges ("normal values") for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update. J Cardiovasc Magn Reson 2020; 22:87. [PMID: 33308262 PMCID: PMC7734766 DOI: 10.1186/s12968-020-00683-3] [Citation(s) in RCA: 244] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 10/26/2020] [Indexed: 01/06/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) enables assessment and quantification of morphological and functional parameters of the heart, including chamber size and function, diameters of the aorta and pulmonary arteries, flow and myocardial relaxation times. Knowledge of reference ranges ("normal values") for quantitative CMR is crucial to interpretation of results and to distinguish normal from disease. Compared to the previous version of this review published in 2015, we present updated and expanded reference values for morphological and functional CMR parameters of the cardiovascular system based on the peer-reviewed literature and current CMR techniques. Further, databases and references for deep learning methods are included.
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Affiliation(s)
- Nadine Kawel-Boehm
- Department of Radiology, Kantonsspital Graubuenden, Loestrasse 170, 7000, Chur, Switzerland
- Institute for Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, InselspitalBern, Switzerland
| | - Scott J Hetzel
- Department of Biostatistics and Medical Informatics, University of Wisconsin, 610 Walnut St, Madison, WI, 53726, USA
| | - Bharath Ambale-Venkatesh
- Department of Radiology, Johns Hopkins University, 600 N Wolfe Street, Baltimore, MD, 21287, USA
| | - Gabriella Captur
- MRC Unit of Lifelong Health and Ageing At UCL, 5-19 Torrington Place, Fitzrovia, London, WC1E 7HB, UK
- Inherited Heart Muscle Conditions Clinic, Royal Free Hospital NHS Foundation Trust, Hampstead, London, NW3 2QG, UK
| | - Christopher J Francois
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - Michael Jerosch-Herold
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Michael Salerno
- Cardiovascular Division, University of Virginia Health System, 1215 Lee Street, Charlottesville, VA, 22908, USA
| | - Shawn D Teague
- Department of Radiology, National Jewish Health, 1400 Jackson St, Denver, CO, 80206, USA
| | - Emanuela Valsangiacomo-Buechel
- Division of Paediatric Cardiology, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032, Zurich, Switzerland
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands
| | - David A Bluemke
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA.
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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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Larrazabal AJ, Martinez C, Glocker B, Ferrante E. Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3813-3820. [PMID: 32746125 DOI: 10.1109/tmi.2020.3005297] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convolutional neural networks or random forest classifiers) incorporate additional post-processing steps to ensure that the resulting masks fulfill expected connectivity constraints. These methods operate under the hypothesis that contiguous pixels with similar aspect should belong to the same class. Even if valid in general, this assumption does not consider more complex priors like topological restrictions or convexity, which cannot be easily incorporated into these methods. Post-DAE leverages the latest developments in manifold learning via denoising autoencoders. First, we learn a compact and non-linear embedding that represents the space of anatomically plausible segmentations. Then, given a segmentation mask obtained with an arbitrary method, we reconstruct its anatomically plausible version by projecting it onto the learnt manifold. The proposed method is trained using unpaired segmentation mask, what makes it independent of intensity information and image modality. We performed experiments in binary and multi-label segmentation of chest X-ray and cardiac magnetic resonance images. We show how erroneous and noisy segmentation masks can be improved using Post-DAE. With almost no additional computation cost, our method brings erroneous segmentations back to a feasible space.
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Abstract
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.
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Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology. CURRENT CARDIOVASCULAR RISK REPORTS 2020. [DOI: 10.1007/s12170-020-00649-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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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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Baskaran L, Maliakal G, Al’Aref SJ, Singh G, Xu Z, Michalak K, Dolan K, Gianni U, van Rosendael A, van den Hoogen I, Han D, Stuijfzand W, Pandey M, Lee BC, Lin F, Pontone G, Knaapen P, Marques H, Bax J, Berman D, Chang HJ, Shaw LJ, Min JK. Identification and Quantification of Cardiovascular Structures From CCTA. JACC Cardiovasc Imaging 2020; 13:1163-1171. [DOI: 10.1016/j.jcmg.2019.08.025] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 08/08/2019] [Accepted: 08/23/2019] [Indexed: 02/04/2023]
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Kim YC, Kim KR, Choe YH. Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105150. [PMID: 31671341 DOI: 10.1016/j.cmpb.2019.105150] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/07/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation. METHODS A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset. RESULTS The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P < 0.001). CONCLUSIONS Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts.
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Affiliation(s)
- Yoon-Chul Kim
- Clinical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Khu Rai Kim
- Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
| | - Yeon Hyeon Choe
- Department of Radiology and HVSI Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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