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G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med 2024; 151:102866. [PMID: 38593684 DOI: 10.1016/j.artmed.2024.102866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 03/20/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Abstract
An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.
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Affiliation(s)
- Sanjeevi G
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India
| | - Uma Gopalakrishnan
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India.
| | | | - Thushara Madathil
- Department of Cardiac Anesthesiology, Amrita Institute of Medical Sciences and Research Center, Kochi, India
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2
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Monkam P, Jin S, Lu W. An efficient annotated data generation method for echocardiographic image segmentation. Comput Biol Med 2022; 149:106090. [PMID: 36115304 DOI: 10.1016/j.compbiomed.2022.106090] [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: 04/27/2022] [Revised: 08/12/2022] [Accepted: 09/03/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND In recent years, deep learning techniques have demonstrated promising performances in echocardiography (echo) data segmentation, which constitutes a critical step in the diagnosis and prognosis of cardiovascular diseases (CVDs). However, their successful implementation requires large number and high-quality annotated samples, whose acquisition is arduous and expertise-demanding. To this end, this study aims at circumventing the tedious, time-consuming and expertise-demanding data annotation involved in deep learning-based echo data segmentation. METHODS We propose a two-phase framework for fast generation of annotated echo data needed for implementing intelligent cardiac structure segmentation systems. First, multi-size and multi-orientation cardiac structures are simulated leveraging polynomial fitting method. Second, the obtained cardiac structures are embedded onto curated endoscopic ultrasound images using Fourier Transform algorithm, resulting in pairs of annotated samples. The practical significance of the proposed framework is validated through using the generated realistic annotated images as auxiliary dataset to pretrain deep learning models for automatic segmentation of left ventricle and left ventricle wall in real echo data, respectively. RESULTS Extensive experimental analyses indicate that compared with training from scratch, fine-tuning after pretraining with the generated dataset always results in significant performance improvement whereby the improvement margins in terms of Dice and IoU can reach 12.9% and 7.74%, respectively. CONCLUSION The proposed framework has great potential to overcome the shortage of labeled data hampering the deployment of deep learning approaches in echo data analysis.
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Affiliation(s)
- Patrice Monkam
- Easysignal Group, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
| | - Songbai Jin
- Easysignal Group, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
| | - Wenkai Lu
- Easysignal Group, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
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3
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Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L. Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. FRONTIERS IN RADIOLOGY 2022; 2:881777. [PMID: 37492680 PMCID: PMC10365116 DOI: 10.3389/fradi.2022.881777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
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Affiliation(s)
- Minh B. Nguyen
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Olivier Villemain
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mark K. Friedberg
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Craig G. Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Luc Mertens
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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Zhang X, Cerna AEU, Stough JV, Chen Y, Carry BJ, Alsaid A, Raghunath S, vanMaanen DP, Fornwalt BK, Haggerty CM. Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:1685-1697. [PMID: 35201510 DOI: 10.1007/s10554-022-02554-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 02/04/2022] [Indexed: 11/26/2022]
Abstract
Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the tenfold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty. We observed that removing segmentations with high uncertainty from 14 to 71% studies reduced volume/mass MAE by 6-10%. The addition of convexity filters improved specificity, efficiently removing < 10% studies with large MAE (16-40%). In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset-segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses-with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.
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Affiliation(s)
- Xiaoyan Zhang
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA
| | - Alvaro E Ulloa Cerna
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA
| | | | - Yida Chen
- Computer Science, Bucknell University, Lewisburg, PA, USA
| | | | - Amro Alsaid
- Heart Institute, Geisinger, Danville, PA, USA
| | - Sushravya Raghunath
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA
| | - David P vanMaanen
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA
- Heart Institute, Geisinger, Danville, PA, USA
- Department of Radiology, Geisinger, Danville, PA, USA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA.
- Heart Institute, Geisinger, Danville, PA, USA.
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Kim S, Park HB, Jeon J, Arsanjani R, Heo R, Lee SE, Moon I, Yoo SK, Chang HJ. Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms. Int J Cardiovasc Imaging 2022; 38:1047-1059. [PMID: 35152371 PMCID: PMC11143010 DOI: 10.1007/s10554-021-02482-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
Abstract
We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.
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Affiliation(s)
- Sekeun Kim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyung-Bok Park
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Catholic Kwandong University International St. Mary's Hospital, Incheon, South Korea
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Reza Arsanjani
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Ran Heo
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, South Korea
| | - Sang-Eun Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Ewha Womans University Seoul Hospital, Seoul, South Korea
| | - Inki Moon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Division of Cardiology, Department of Internal Medicine, Soonchunghyang University Bucheon Hospital, Bucheon, South Korea
| | - Sun Kook Yoo
- Department of Medical Engineering, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Cardiology, Department of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
- Ontact Health Co., Ltd., Seoul, South Korea.
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An improved semantic segmentation with region proposal network for cardiac defect interpretation. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07217-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Sirjani N, Moradi S, Oghli MG, Hosseinsabet A, Alizadehasl A, Yadollahi M, Shiri I, Shabanzadeh A. Automatic cardiac evaluations using a deep video object segmentation network. Insights Imaging 2022; 13:69. [PMID: 35394221 PMCID: PMC8994013 DOI: 10.1186/s13244-022-01212-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/17/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network. RESULTS The proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively. CONCLUSION This study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings.
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Affiliation(s)
- Nasim Sirjani
- Research and Development Department, Med Fanavarn Plus Co., 10th St. Shahid Babaee Blvd., Payam Special Zone, 3187411213, Karaj, Iran
| | - Shakiba Moradi
- Research and Development Department, Med Fanavarn Plus Co., 10th St. Shahid Babaee Blvd., Payam Special Zone, 3187411213, Karaj, Iran.
| | - Mostafa Ghelich Oghli
- Research and Development Department, Med Fanavarn Plus Co., 10th St. Shahid Babaee Blvd., Payam Special Zone, 3187411213, Karaj, Iran.,Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Ali Hosseinsabet
- Cardiology Department, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, I.R., Iran
| | - Azin Alizadehasl
- Echocardiography and Cardiogenetic Research Centers, Cardio-Oncology Department, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
| | - Mona Yadollahi
- Echocardiography and Cardiogenetic Research Centers, Cardio-Oncology Department, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Ali Shabanzadeh
- Research and Development Department, Med Fanavarn Plus Co., 10th St. Shahid Babaee Blvd., Payam Special Zone, 3187411213, Karaj, Iran
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree. Int J Comput Assist Radiol Surg 2021; 16:1493-1505. [PMID: 34101135 DOI: 10.1007/s11548-021-02421-1] [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: 12/24/2020] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial. METHODS An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result. RESULTS The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 [Formula: see text] 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 [Formula: see text] 0.024 mm, 6.74 [Formula: see text] 0.013 mm, and 0.901 [Formula: see text] 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively. CONCLUSION Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs).
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Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography. Med Image Anal 2021; 71:102042. [PMID: 33784600 DOI: 10.1016/j.media.2021.102042] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/19/2022]
Abstract
Paediatric echocardiography is a standard method for screening congenital heart disease (CHD). The segmentation of paediatric echocardiography is essential for subsequent extraction of clinical parameters and interventional planning. However, it remains a challenging task due to (1) the considerable variation of key anatomic structures, (2) the poor lateral resolution affecting accurate boundary definition, (3) the existence of speckle noise and artefacts in echocardiographic images. In this paper, we propose a novel deep network to address these challenges comprehensively. We first present a dual-path feature extraction module (DP-FEM) to extract rich features via a channel attention mechanism. A high- and low-level feature fusion module (HL-FFM) is devised based on spatial attention, which selectively fuses rich semantic information from high-level features with spatial cues from low-level features. In addition, a hybrid loss is designed to deal with pixel-level misalignment and boundary ambiguities. Based on the segmentation results, we derive key clinical parameters for diagnosis and treatment planning. We extensively evaluate the proposed method on 4,485 two-dimensional (2D) paediatric echocardiograms from 127 echocardiographic videos. The proposed method consistently achieves better segmentation performance than other state-of-the-art methods, whichdemonstratesfeasibility for automatic segmentation and quantitative analysis of paediatric echocardiography. Our code is publicly available at https://github.com/end-of-the-century/Cardiac.
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Barzegar N, Khatibi T, Hosseinsabet A. Proposing novel methods for simultaneous cardiac cycle phase identification and estimating maximal and minimal left atrial volume (LAV) from apical four-chamber view in 2-D echocardiography. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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12
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Kheradvar A, Jafarkhani H, Guy TS, Finn JP. Prospect of artificial intelligence for the assessment of cardiac function and treatment of cardiovascular disease. Future Cardiol 2020; 17:183-187. [PMID: 32933328 DOI: 10.2217/fca-2020-0128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Arash Kheradvar
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, Irvine, CA 92697, USA
| | - Hamid Jafarkhani
- Center for Pervasive Communications & Computing, University of California, Irvine, Irvine, CA 92697, USA
| | - Thomas Sloane Guy
- Division of Cardiac Surgery, Department of Surgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - John Paul Finn
- Department of Radiological Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
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