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Batool S, Taj IA, Ghafoor M. Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods. Diagnostics (Basel) 2023; 13:2155. [PMID: 37443550 DOI: 10.3390/diagnostics13132155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/10/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.
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Affiliation(s)
- Samana Batool
- Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad 44000, Pakistan
| | - Imtiaz Ahmad Taj
- Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad 44000, Pakistan
| | - Mubeen Ghafoor
- School of Computer Science, University of Lincoln, Brayford Way, Brayford, Pool, Lincoln LN6 7TS, UK
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Salehi M, Vafaei Sadr A, Mahdavi SR, Arabi H, Shiri I, Reiazi R. Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer. J Digit Imaging 2023; 36:574-587. [PMID: 36417026 PMCID: PMC10039214 DOI: 10.1007/s10278-022-00732-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 07/04/2022] [Accepted: 07/18/2022] [Indexed: 11/25/2022] Open
Abstract
In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and motion within an image deformation, was proposed. Data included 57 CT scans (7202 2D slices) of patients with LACC randomly divided into the train (n = 42) and test (n = 15) datasets. In addition to CT images and the corresponding RT structure (bladder, cervix, and rectum), the bone was segmented, and the coaches were eliminated. The correlated stochastic field was simulated using the same size as the target image (used for deformation) to produce the general random deformation. The deformation field was optimized to have a maximum amplitude in the rectum region, a moderate amplitude in the bladder region, and an amplitude as minimum as possible within bony structures. The DIRNet is a convolutional neural network that consists of convolutional regressors, spatial transformation, as well as resampling blocks. It was implemented by different parameters. Mean Dice indices of 0.89 ± 0.02, 0.96 ± 0.01, and 0.93 ± 0.02 were obtained for the cervix, bladder, and rectum (defined as at-risk organs), respectively. Furthermore, a mean average symmetric surface distance of 1.61 ± 0.46 mm for the cervix, 1.17 ± 0.15 mm for the bladder, and 1.06 ± 0.42 mm for the rectum were achieved. In addition, a mean Jaccard of 0.86 ± 0.04 for the cervix, 0.93 ± 0.01 for the bladder, and 0.88 ± 0.04 for the rectum were observed on the test dataset (15 subjects). Deep learning-based non-rigid image registration is, therefore, proposed for the high-dose-rate brachytherapy in inter-fraction cervical cancer since it outperformed conventional algorithms.
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Affiliation(s)
- Mohammad Salehi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Vafaei Sadr
- Department of Theoretical Physics and Center for Astroparticle Physics, University of Geneva, Geneva, Switzerland
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
| | - Reza Reiazi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Division of Radiation Oncology, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA.
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Kuo HC, Chen SH, Chen YH, Lin YC, Chang CY, Wu YC, Wang TD, Chang LS, Tai IH, Hsieh KS. Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone. Front Cardiovasc Med 2023; 9:1000374. [PMID: 36741838 PMCID: PMC9895373 DOI: 10.3389/fcvm.2022.1000374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/30/2022] [Indexed: 01/21/2023] Open
Abstract
Introduction Kawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images. Methods Specifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework. Results The experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5. Conclusions Scaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution.
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Affiliation(s)
- Ho-Chang Kuo
- Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, Taiwan
| | - Shih-Hsin Chen
- Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan,*Correspondence: Shih-Hsin Chen ✉
| | - Yi-Hui Chen
- Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, Taiwan,Department of Information Management, Chang Gung University, Kaohsiung, Taiwan
| | - Yu-Chi Lin
- Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, Taiwan
| | - Chih-Yung Chang
- Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan
| | - Yun-Cheng Wu
- Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan
| | - Tzai-Der Wang
- Department of E-Sport Technology Management, Cheng Shiu University, Kaohsiung, Taiwan
| | - Ling-Sai Chang
- Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, Taiwan
| | - I-Hsin Tai
- Department of Medicine, College of Medicine, China Medical University, Taichung, Taiwan,Department of Pediatric Cardiology, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan
| | - Kai-Sheng Hsieh
- Center of Structure and Congenital Heart Disease/Ultrasound and Department of Cardiology, Children's Hospital, China Medical University, Taichung, Taiwan,Kai-Sheng Hsieh ✉
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Belfilali H, Bousefsaf F, Messadi M. Left ventricle analysis in echocardiographic images using transfer learning. Phys Eng Sci Med 2022; 45:1123-1138. [PMID: 36131173 DOI: 10.1007/s13246-022-01179-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/13/2022] [Indexed: 12/15/2022]
Abstract
The segmentation of cardiac boundaries, specifically Left Ventricle (LV) segmentation in 2D echocardiographic images, is a critical step in LV segmentation and cardiac function assessment. These images are generally of poor quality and present low contrast, making daily clinical delineation difficult, time-consuming, and often inaccurate. Thus, it is necessary to design an intelligent automatic endocardium segmentation system. The present work aims to examine and assess the performance of some deep learning-based architectures such as U-Net1, U-Net2, LinkNet, Attention U-Net, and TransUNet using the public CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) dataset. The adopted approach emphasizes the advantage of using transfer learning and resorting to pre-trained backbones in the encoder part of a segmentation network for echocardiographic image analysis. The experimental findings indicated that the proposed framework with the [Formula: see text]-[Formula: see text] is quite promising; it outperforms other more recent approaches with a Dice similarity coefficient of 93.30% and a Hausdorff Distance of 4.01 mm. In addition, a good agreement between the clinical indices calculated from the automatic segmentation and those calculated from the ground truth segmentation. For instance, the mean absolute errors for the left ventricular end-diastolic volume, end-systolic volume, and ejection fraction are equal to 7.9 ml, 5.4 ml, and 6.6%, respectively. These results are encouraging and point out additional perspectives for further improvement.
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Affiliation(s)
- Hafida Belfilali
- Laboratory of Biomedical Engineering, Faculty of technology, University of Tlemcen, 13000, Tlemcen, Algeria.
| | - Frédéric Bousefsaf
- Laboratoire de Conception, Optimisation et Modélisation des Systèmes, LCOMS EA 7306, Université de Lorraine, 57000, Metz, France.
| | - Mahammed Messadi
- Laboratory of Biomedical Engineering, Faculty of technology, University of Tlemcen, 13000, Tlemcen, Algeria
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