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Matsunaga T, Kono A, Matsuo H, Kitagawa K, Nishio M, Hashimura H, Izawa Y, Toba T, Ishikawa K, Katsuki A, Ohmura K, Murakami T. Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography. Acad Radiol 2024; 31:822-829. [PMID: 37914626 DOI: 10.1016/j.acra.2023.09.014] [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: 07/28/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 11/03/2023]
Abstract
RATIONALE AND OBJECTIVES Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. MATERIALS AND METHODS We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT). We excluded patients with metal implants, pleural effusion, history of thoracic surgery, or malignancy. Thus, the data of 191 patients were used. We generated PFCIs from the projection of three-dimensional CT images, wherein fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. RESULTS The mean SSIM, MSE, and MAE were 8.56 × 10-1, 1.28 × 10-2, and 3.57 × 10-2, respectively, for the proposed model, and 7.62 × 10-1, 1.98 × 10-2, and 5.04 × 10-2, respectively, for the single CycleGAN-based model. CONCLUSION PFCIs generated from CXRs with the proposed model showed better performance than those generated with the single model. The evaluation of PF without CT may be possible using the proposed method.
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Affiliation(s)
- Takaaki Matsunaga
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Atsushi Kono
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Hidetoshi Matsuo
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Kaoru Kitagawa
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Japan (K.K., K.I.)
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.).
| | - Hiromi Hashimura
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Yu Izawa
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan (Y.I., T.T.)
| | - Takayoshi Toba
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan (Y.I., T.T.)
| | - Kazuki Ishikawa
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Japan (K.K., K.I.)
| | | | | | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
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Santos da Silva G, Casanova D, Oliva JT, Rodrigues EO. Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network. Med Eng Phys 2024; 124:104104. [PMID: 38418017 DOI: 10.1016/j.medengphy.2024.104104] [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: 03/15/2022] [Revised: 08/17/2023] [Accepted: 01/09/2024] [Indexed: 03/01/2024]
Abstract
In recent years, research has highlighted the association between increased adipose tissue surrounding the human heart and elevated susceptibility to cardiovascular diseases such as atrial fibrillation and coronary heart disease. However, the manual segmentation of these fat deposits has not been widely implemented in clinical practice due to the substantial workload it entails for medical professionals and the associated costs. Consequently, the demand for more precise and time-efficient quantitative analysis has driven the emergence of novel computational methods for fat segmentation. This study presents a novel deep learning-based methodology that offers autonomous segmentation and quantification of two distinct types of cardiac fat deposits. The proposed approach leverages the pix2pix network, a generative conditional adversarial network primarily designed for image-to-image translation tasks. By applying this network architecture, we aim to investigate its efficacy in tackling the specific challenge of cardiac fat segmentation, despite not being originally tailored for this purpose. The two types of fat deposits of interest in this study are referred to as epicardial and mediastinal fats, which are spatially separated by the pericardium. The experimental results demonstrated an average accuracy of 99.08% and f1-score 98.73 for the segmentation of the epicardial fat and 97.90% of accuracy and f1-score of 98.40 for the mediastinal fat. These findings represent the high precision and overlap agreement achieved by the proposed methodology. In comparison to existing studies, our approach exhibited superior performance in terms of f1-score and run time, enabling the images to be segmented in real time.
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Affiliation(s)
- Guilherme Santos da Silva
- Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil
| | - Dalcimar Casanova
- Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil
| | - Jefferson Tales Oliva
- Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil
| | - Erick Oliveira Rodrigues
- Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil; Graduate Program of Production and Systems Engineering, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil.
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Li K, Liu B, Wang Z, Li Y, Li H, Wu S, Li Z. Quantitative characterization of zebrafish development based on multiple classifications using Mueller matrix OCT. BIOMEDICAL OPTICS EXPRESS 2023; 14:2889-2904. [PMID: 37342688 PMCID: PMC10278635 DOI: 10.1364/boe.488614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/23/2023]
Abstract
Organ development analysis plays an important role in assessing an individual' s growth health. In this study, we present a non-invasive method for the quantitative characterization of zebrafish multiple organs during their growth, utilizing Mueller matrix optical coherence tomography (Mueller matrix OCT) in combination with deep learning. Firstly, Mueller matrix OCT was employed to acquire 3D images of zebrafish during development. Subsequently, a deep learning based U-Net network was applied to segment various anatomical structures, including the body, eyes, spine, yolk sac, and swim bladder of the zebrafish. Following segmentation, the volume of each organ was calculated. Finally, the development and proportional trends of zebrafish embryos and organs from day 1 to day 19 were quantitatively analyzed. The obtained quantitative results revealed that the volume development of the fish body and individual organs exhibited a steady growth trend. Additionally, smaller organs, such as the spine and swim bladder, were successfully quantified during the growth process. Our findings demonstrate that the combination of Mueller matrix OCT and deep learning effectively quantify the development of various organs throughout zebrafish embryonic development. This approach offers a more intuitive and efficient monitoring method for clinical medicine and developmental biology studies.
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Affiliation(s)
- Ke Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Bin Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Zaifan Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Yao Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Hui Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Shulian Wu
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Zhifang Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
- Bionovel Lab, Guangzhou, Guangdong, 510407, China
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A Heart Segmentation Algorithm Based on Dynamic Ultrasound. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1485584. [PMID: 35757484 PMCID: PMC9232347 DOI: 10.1155/2022/1485584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/23/2022] [Accepted: 06/07/2022] [Indexed: 12/04/2022]
Abstract
The heart is one of the most important organs of the human body. The role of the heart is to promote blood flow and provide sufficient blood flow to organs and tissues. The research on the heart has important theoretical and clinical significance. Because of the noninvasive and intuitive display of ultrasound image, it can dynamically obtain the heart state and has become the main means to detect the heart dynamics. We analyze the characteristics of cardiac ultrasound image from the medical point of view and signal processing. The heart movement is periodic and rhythmic. The image signal can be decomposed. Firstly, the image is decomposed into high- and low-frequency signals to highlight different dimensional information. Then, the attention model was introduced, focusing on the heart region. Finally, the multidimensional network carrying model was established to achieve cardiac segmentation. The experimental results show that the AOM of the algorithm proposed in this paper reaches 92%, which has a certain degree of advancement and can assist doctors to make accurate diagnosis.
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Deep Learning-Based Approach for the Automatic Quantification of Epicardial Adipose Tissue from Non-Contrast CT. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10036-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Ge Y, Zhang Q, Sun Y, Shen Y, Wang X. Grayscale medical image segmentation method based on 2D&3D object detection with deep learning. BMC Med Imaging 2022; 22:33. [PMID: 35220942 PMCID: PMC8883636 DOI: 10.1186/s12880-022-00760-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/22/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. METHODS Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes' bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. RESULTS Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance. CONCLUSIONS The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.
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Affiliation(s)
- Yunfei Ge
- School of Mechanical Engineering, Tongji University, Shanghai, China
| | - Qing Zhang
- School of Mechanical Engineering, Tongji University, Shanghai, China
| | - Yuantao Sun
- School of Mechanical Engineering, Tongji University, Shanghai, China.
| | - Yidong Shen
- Department of Orthopaedics, The First People's Hospital of Yancheng, Yancheng, China
| | - Xijiong Wang
- Shanghai Bojin Electric Instrument and Device Co., Ltd, Shanghai, China
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Hoori A, Hu T, Lee J, Al-Kindi S, Rajagopalan S, Wilson DL. Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans. Sci Rep 2022; 12:2276. [PMID: 35145186 PMCID: PMC8831577 DOI: 10.1038/s41598-022-06351-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/11/2022] [Indexed: 11/12/2022] Open
Abstract
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a HU-attention-window with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel look ahead slab-of-slices with bisection ("bisect") in which we split the heart into halves and sequenced the lower half from bottom-to-middle and the upper half from top-to-middle, thereby presenting an always increasing curvature of the sac to the network. EAT volume was obtained by thresholding voxels within the sac in the fat window (- 190/- 30-HU). Compared to manual segmentation, our algorithm gave excellent results with volume Dice = 88.52% ± 3.3, slice Dice = 87.70% ± 7.5, EAT error = 0.5% ± 8.1, and R = 98.52% (p < 0.001). HU-attention-window and bisect improved Dice volume scores by 0.49% and 3.2% absolute, respectively. Variability between analysts was comparable to variability with DeepFat. Results compared favorably to those of previous publications.
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Affiliation(s)
- Ammar Hoori
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Tao Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Juhwan Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Sadeer Al-Kindi
- Department of Cardiology, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Sanjay Rajagopalan
- Department of Cardiology, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Zhang B, Pas KE, Ijaseun T, Cao H, Fei P, Lee J. Automatic Segmentation and Cardiac Mechanics Analysis of Evolving Zebrafish Using Deep Learning. Front Cardiovasc Med 2021; 8:675291. [PMID: 34179138 PMCID: PMC8221393 DOI: 10.3389/fcvm.2021.675291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/20/2021] [Indexed: 01/04/2023] Open
Abstract
Background: In the study of early cardiac development, it is essential to acquire accurate volume changes of the heart chambers. Although advanced imaging techniques, such as light-sheet fluorescent microscopy (LSFM), provide an accurate procedure for analyzing the heart structure, rapid, and robust segmentation is required to reduce laborious time and accurately quantify developmental cardiac mechanics. Methods: The traditional biomedical analysis involving segmentation of the intracardiac volume occurs manually, presenting bottlenecks due to enormous data volume at high axial resolution. Our advanced deep-learning techniques provide a robust method to segment the volume within a few minutes. Our U-net-based segmentation adopted manually segmented intracardiac volume changes as training data and automatically produced the other LSFM zebrafish cardiac motion images. Results: Three cardiac cycles from 2 to 5 days postfertilization (dpf) were successfully segmented by our U-net-based network providing volume changes over time. In addition to understanding each of the two chambers' cardiac function, the ventricle and atrium were separated by 3D erode morphology methods. Therefore, cardiac mechanical properties were measured rapidly and demonstrated incremental volume changes of both chambers separately. Interestingly, stroke volume (SV) remains similar in the atrium while that of the ventricle increases SV gradually. Conclusion: Our U-net-based segmentation provides a delicate method to segment the intricate inner volume of the zebrafish heart during development, thus providing an accurate, robust, and efficient algorithm to accelerate cardiac research by bypassing the labor-intensive task as well as improving the consistency in the results.
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Affiliation(s)
- Bohan Zhang
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States.,School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Kristofor E Pas
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States
| | - Toluwani Ijaseun
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States
| | - Hung Cao
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Peng Fei
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Juhyun Lee
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States.,Department of Medical Education, Texas Christian University (TCU) and University of North Texas Health Science Center (UNTHSC) School of Medicine, Fort Worth, TX, United States
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Yang G, Zhang H, Firmin D, Li S. Recent advances in artificial intelligence for cardiac imaging. Comput Med Imaging Graph 2021; 90:101928. [PMID: 33965746 DOI: 10.1016/j.compmedimag.2021.101928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Guang Yang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK; Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK.
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 510006, China.
| | - David Firmin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK; Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON, Canada; Digital Imaging Group, London, ON, Canada
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Guo Z, Li W, Dai J, Xiang J, Dan G. Facial imaging and landmark detection technique for objective assessment of unilateral peripheral facial paralysis. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.1872108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Zhexiao Guo
- School of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China
| | - Weiben Li
- School of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China
| | - Juan Dai
- Department of stomatology, Shenzhen University General Hospital, Shenzhen, China
| | - Jianghuai Xiang
- School of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China
| | - Guo Dan
- School of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China
- Shenzhen Institute of Neuroscience, Shenzhen, China
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Chagas JVSD, de A. Rodrigues D, Ivo RF, Hassan MM, de Albuquerque VHC, Filho PPR. A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system. JOURNAL OF REAL-TIME IMAGE PROCESSING 2021; 18:1099-1114. [PMID: 33747237 PMCID: PMC7960401 DOI: 10.1007/s11554-021-01086-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 02/17/2021] [Indexed: 05/05/2023]
Abstract
Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in chest X-ray images. The dataset used has 6000 chest X-ray images of children, and three medical specialists performed the validations. In this work, twelve different architectures of Convolutional Neural Networks (CNNs) trained on ImageNet were adapted to operate as the resource extractors. Subsequently, the CNNs were combined with consolidated learning methods, such as k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The results showed that the VGG19 architecture with the SVM classifier using the RBF kernel was the best model to detect pneumonia in these chest radiographs. This combination reached 96.47%, 96.46%, and 96.46% for Accuracy, F1 score, and Precision values, respectively. Compared to other works in the literature, the proposed approach had better results for the metrics used. These results show that this approach for the detection of pneumonia in children using a real-time IoT system is efficient and is, therefore, a potential tool to aid in medical diagnoses. This approach will allow specialists to obtain faster and more accurate results and thus provide the appropriate treatment.
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Affiliation(s)
- João Victor S. das Chagas
- Federal Institute of Education, Science and Technology of Ceará, LAPISCO, Fortaleza, CE 60040-215 Brazil
| | - Douglas de A. Rodrigues
- Federal Institute of Education, Science and Technology of Ceará, LAPISCO, Fortaleza, CE 60040-215 Brazil
| | - Roberto F. Ivo
- Federal Institute of Education, Science and Technology of Ceará, LAPISCO, Fortaleza, CE 60040-215 Brazil
| | - Mohammad Mehedi Hassan
- Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, 11543 Riyadh, Saudi Arabia
| | - Victor Hugo C. de Albuquerque
- Department of Computer Science, Federal Institute of Education, Science and Technology of Ceará, 60040-215 Fortaleza, CE Brazil
- Department of Teleinformatics Engineering, Federal University of Ceará, 60020-181 Fortaleza, CE Brazil
| | - Pedro P. Rebouças Filho
- Department of Computer Science, Federal Institute of Education, Science and Technology of Ceará, 60040-215 Fortaleza, CE Brazil
- Department of Teleinformatics Engineering, Federal University of Ceará, 60020-181 Fortaleza, CE Brazil
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A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8847144. [PMID: 32802300 PMCID: PMC7416266 DOI: 10.1155/2020/8847144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/27/2020] [Accepted: 07/14/2020] [Indexed: 01/19/2023]
Abstract
Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with traditional models. However, no models that contain comprehensive risk factors were assessed, and there are limited data on the comparison of different 3D STE parameters. In this study, we sought to build a machine learning model for predicting LVR in AMI patients after effective percutaneous coronary intervention (PCI) that contains the majority of the clinical risk factors and compare 3D STE parameters values for LVR prediction. We enrolled 135 first-onset AMI patients (120 males, mean age 54 ± 9 years). All patients went through a 3D STE and a traditional transthoracic echocardiography 24 hours after reperfusion. A second echocardiography was repeated at the three-month follow-up to detect LVR (defined as a 20 percent increase in left ventricular end-diastolic volume). Six models were constructed using 15 risk factors. A receiver operator characteristic curve and four performance measurements were used as evaluation methods. Feature importance was used to compare 3D STE parameters. 26 patients (19.3%) had LVR. Our evaluation showed that RF can best predict LVR with the best AUC of 0.96. 3D GLS was the most valuable 3D STE parameters, followed by GCS, global area strain, and global radial strain (feature importance 0.146, 0.089, 0.087, and 0.069, respectively). To sum up, RF models can accurately predict the LVR after AMI, and 3D GLS was the best 3D STE parameters in predicting the LVR.
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