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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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Song Y, Tan Y, Deng M, Shan W, Zheng W, Zhang B, Cui J, Feng L, Shi L, Zhang M, Liu Y, Sun Y, Yi W. Epicardial adipose tissue, metabolic disorders, and cardiovascular diseases: recent advances classified by research methodologies. MedComm (Beijing) 2023; 4:e413. [PMID: 37881786 PMCID: PMC10594046 DOI: 10.1002/mco2.413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
Epicardial adipose tissue (EAT) is located between the myocardium and visceral pericardium. The unique anatomy and physiology of the EAT determines its great potential in locally influencing adjacent tissues such as the myocardium and coronary arteries. Classified by research methodologies, this study reviews the latest research progress on the role of EAT in cardiovascular diseases (CVDs), particularly in patients with metabolic disorders. Studies based on imaging techniques demonstrated that increased EAT amount in patients with metabolic disorders is associated with higher risk of CVDs and increased mortality. Then, in-depth profiling studies indicate that remodeled EAT may serve as a local mediator of the deleterious effects of cardiometabolic conditions and plays a crucial role in CVDs. Further, in vitro coculture studies provided preliminary evidence that the paracrine effect of remodeled EAT on adjacent cardiomyocytes can promote the occurrence and progression of CVDs. Considering the important role of EAT in CVDs, targeting EAT might be a potential strategy to reduce cardiovascular risks. Several interventions have been proved effective in reducing EAT amount. Our review provides valuable insights of the relationship between EAT, metabolic disorders, and CVDs, as well as an overview of the methodological constructs of EAT-related studies.
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Affiliation(s)
- Yujie Song
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Yanzhen Tan
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Meng Deng
- Department of General MedicineXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Wenju Shan
- Department of General MedicineXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Wenying Zheng
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Bing Zhang
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Jun Cui
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Lele Feng
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Lei Shi
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Miao Zhang
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Yingying Liu
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Yang Sun
- Department of General MedicineXijing HospitalThe Fourth Military Medical UniversityXi'anChina
| | - Wei Yi
- Department of Cardiovascular SurgeryXijing HospitalThe Fourth Military Medical UniversityXi'anChina
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4
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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices. LAND 2022. [DOI: 10.3390/land11081138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.
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6
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Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Epicardial and pericardial adipose tissues (EAT and PAT), which are located around the heart, have been linked to coronary atherosclerosis, cardiomyopathy, coronary artery disease, and other cardiovascular diseases. Additionally, the volume and thickness of EAT are good predictors of CVD risk levels. Manual quantification of these tissues is a tedious and error-prone process. This paper presents a comprehensive and critical overview of research on the epicardial and pericardial adipose tissue segmentation and quantification methods, evaluates their effectiveness in terms of segmentation time and accuracy, provides a critical comparison of the methods, and presents ongoing and future challenges in the field. Described methods are classified into pericardial adipose tissue segmentation, direct epicardial adipose tissue segmentation, and epicardial adipose tissue segmentation via pericardium delineation. A comprehensive categorization of the underlying methods is conducted with insights into their evolution from traditional image processing methods to recent deep learning-based methods. The paper also provides an overview of the research on the clinical significance of epicardial and pericardial adipose tissues as well as the terminology and definitions used in the medical literature.
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Bray JJH, Hanif MA, Alradhawi M, Ibbetson J, Dosanjh SS, Smith SL, Ahmad M, Pimenta D. Machine learning applications in cardiac computed tomography: a composite systematic review. EUROPEAN HEART JOURNAL OPEN 2022; 2:oeac018. [PMID: 35919128 PMCID: PMC9242067 DOI: 10.1093/ehjopen/oeac018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/10/2022] [Indexed: 12/02/2022]
Abstract
Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.
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Affiliation(s)
- Jonathan James Hyett Bray
- Institute of Life Sciences 2, Swansea University Medical, School , Swansea, UK
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | - Moghees Ahmad Hanif
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Jacob Ibbetson
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
| | | | - Sabrina Lucy Smith
- Barts and the London School of Medicine and Dentistry , London E1 2AD, UK
| | - Mahmood Ahmad
- Cardiology Department, Royal Free Hospital, Royal Free London NHS Foundation Trust , London, UK
- University College London Medical School , London WC1E 6DE, UK
| | - Dominic Pimenta
- Richmond Research Institute, St George’s Hospital, University of London , Cranmer Terrace, Tooting, London SW17 0RE, UK
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8
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Greco F, Salgado R, Van Hecke W, Del Buono R, Parizel PM, Mallio CA. Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review. Quant Imaging Med Surg 2022; 12:2075-2089. [PMID: 35284252 PMCID: PMC8899943 DOI: 10.21037/qims-21-945] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 12/03/2021] [Indexed: 07/24/2023]
Abstract
The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.
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Affiliation(s)
- Federico Greco
- U.O.C. Diagnostica per Immagini Territoriale Aziendale, Cittadella della Salute Azienda Sanitaria Locale di Lecce, Lecce, Italy
| | - Rodrigo Salgado
- Department of Radiology, Antwerp University Hospital (UZA), Edegem, Belgium
| | - Wim Van Hecke
- AI Supported Modelling in Clinical Sciences (AIMS), Vrije Universiteit Brussel, 1050 Brussels, Belgium and founder of icoMetrix, Leuven, Belgium
| | - Romualdo Del Buono
- Unit of Anesthesia, Resuscitation, Intensive Care and Pain Management, ASST Gaetano Pini, Milano, Italy
| | - Paul M. Parizel
- Royal Perth Hospital & University of Western Australia, Perth, Western Australia, Australia
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Siriapisith T, Kusakunniran W, Haddawy P. A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images. PeerJ Comput Sci 2021; 7:e806. [PMID: 34977354 PMCID: PMC8670388 DOI: 10.7717/peerj-cs.806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/12/2021] [Indexed: 06/14/2023]
Abstract
Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.
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Affiliation(s)
- Thanongchai Siriapisith
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
- Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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Monti CB, Capra D, Zanardo M, Guarnieri G, Schiaffino S, Secchi F, Sardanelli F. CT-derived epicardial adipose tissue density: Systematic review and meta-analysis. Eur J Radiol 2021; 143:109902. [PMID: 34482178 DOI: 10.1016/j.ejrad.2021.109902] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/22/2021] [Accepted: 08/05/2021] [Indexed: 01/16/2023]
Abstract
PURPOSE The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables. METHOD A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume. RESULTS Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of -85.86 HU (95% confidence interval [95% CI] -91.84, -79.89 HU), being -86.40 HU (95% CI -112.69, -60.12 HU) in healthy subjects and -80.71 HU (95% CI -87.43, -73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p < 0.001 and p< 0.001 respectively). CONCLUSIONS Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.
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Affiliation(s)
- Caterina B Monti
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy.
| | - Davide Capra
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy
| | - Moreno Zanardo
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy
| | - Gianluca Guarnieri
- Postgraduation School in Cardiology, Università degli Studi di Milano, Milano, Italy
| | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milano, Italy
| | - Francesco Secchi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy; Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milano, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy; Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milano, Italy
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11
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Rodrigues EO, Rodrigues LO, Lima JJ, Casanova D, Favarim F, Dosciatti ER, Pegorini V, Oliveira LSN, Morais FFC. X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing. Biomed Phys Eng Express 2021; 7. [PMID: 34256366 DOI: 10.1088/2057-1976/ac13ba] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/13/2021] [Indexed: 11/11/2022]
Abstract
This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.
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Affiliation(s)
- E O Rodrigues
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - L O Rodrigues
- Graduate Program of Applied Sciences to Health Products, Universidade Federal Fluminense (UFF), Niteroi, Rio de Janeiro, Brazil
| | - J J Lima
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - D Casanova
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - F Favarim
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - E R Dosciatti
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - V Pegorini
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil
| | - L S N Oliveira
- Primary Health Care, Pato Branco Prefecture, Parana, Brazil
| | - F F C Morais
- Innovation Office, Mass General Brigham Hospital, Cambridge, Massachusetts, United States of America
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12
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Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2021; 116:2040-2054. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/29/2019] [Accepted: 01/23/2020] [Indexed: 12/23/2022] Open
Abstract
ABSTRACT Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
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Affiliation(s)
- Evangelos K Oikonomou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Department of Internal Medicine, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Musib Siddique
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Caristo Diagnostics Ltd., Oxford, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Oxford Centre of Research Excellence, British Heart Foundation, Oxford, UK.,Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK
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13
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Zhang L, Sun J, Jiang B, Wang L, Zhang Y, Xie X. Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review. Eur J Hybrid Imaging 2021; 5:14. [PMID: 34312735 PMCID: PMC8313612 DOI: 10.1186/s41824-021-00107-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/09/2021] [Indexed: 12/22/2022] Open
Abstract
Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging. Supplementary Information The online version contains supplementary material available at 10.1186/s41824-021-00107-0.
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Affiliation(s)
- Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Jianqing Sun
- Shukun (Beijing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing, 100102, China
| | - Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Lingyun Wang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Yaping Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
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14
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Kalaivani K, Uma Maheswari N, Venkatesh R. Classification of heart disease using mfo based neural network on mri images. Curr Med Imaging 2021; 17:1114-1127. [PMID: 33573572 DOI: 10.2174/1573405617666210126153920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 10/03/2020] [Accepted: 10/15/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Cardiovascular disease (CVD) is one of the primary diseases that cause death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, for every 34 seconds the people were died due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. AIMS The main aim of this work is to improve the performance of heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. OBJECTIVE The objective of this investigation is to diagnosis heart disease using feature extraction and reduction based classification using image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system in co-operates three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. METHOD The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image is calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image is given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image and determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with regression nature. Due to the regression property the network is well trained with the feature. The Generalized regression neural network is used for classifying the heart disease. RESULTS The proposed method achieves the accuracy of 96.23%, sensitivity 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. CONCLUSION In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods either the feature extraction based classification or the feature reduction based classification.
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Affiliation(s)
- Kalaivani 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 computer Science and Engineering, PSNA college of Engineering and Technology,Dindigul,Tamil Nadu. India
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15
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Fast fully automatic heart fat segmentation in computed tomography datasets. Comput Med Imaging Graph 2019; 80:101674. [PMID: 31884225 DOI: 10.1016/j.compmedimag.2019.101674] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/26/2019] [Accepted: 10/24/2019] [Indexed: 11/24/2022]
Abstract
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new approach to the segmentation of cardiac fat from Computed Tomography (CT) images. The study employs a clustering algorithm called Floor of Log (FoL). The advantage of this method is the significant drop in segmentation time. Support Vector Machine was used to learn the best FoL algorithm parameter as well as mathematical morphology techniques for noise removal. The time to segment cardiac fat on a CT is only 2.01 s on average. In contrast, literature works require more than one hour to perform segmentation. Therefore, this job is one of the fastest to segment an exam completely. The value of the Accuracy metric was 93.45% and Specificity of 95.52%. The proposed approach is automatic and requires less computational effort. With these results, the use of this approach for the segmentation of cardiac fat proves to be efficient, besides having good application times. Therefore, it has the potential to be a medical diagnostic aid tool. Consequently, it is possible to help experts achieve faster and more accurate results.
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16
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Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105748] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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17
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Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9204338] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.
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18
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Militello C, Rundo L, Toia P, Conti V, Russo G, Filorizzo C, Maffei E, Cademartiri F, La Grutta L, Midiri M, Vitabile S. A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans. Comput Biol Med 2019; 114:103424. [PMID: 31521896 DOI: 10.1016/j.compbiomed.2019.103424] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 01/23/2023]
Abstract
Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.
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Affiliation(s)
- Carmelo Militello
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), Cefalù (PA), Italy.
| | - Leonardo Rundo
- University of Cambridge, Department of Radiology, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, Cambridge, United Kingdom; Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), Cefalù (PA), Italy
| | - Patrizia Toia
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Italy
| | - Vincenzo Conti
- Faculty of Engineering and Architecture, University of Enna KORE, Enna, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), Cefalù (PA), Italy
| | - Clarissa Filorizzo
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Italy
| | - Erica Maffei
- Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy
| | | | - Ludovico La Grutta
- Department of Health Promotion Sciences Maternal and Infantile Care, Internal Medicine and Medical Specialities (ProMISE), University of Palermo, Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Italy
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Rodrigues ÉO, Rodrigues LO, Oliveira LSN, Conci A, Liatsis P. Automated recognition of the pericardium contour on processed CT images using genetic algorithms. Comput Biol Med 2017; 87:38-45. [PMID: 28549293 DOI: 10.1016/j.compbiomed.2017.05.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/11/2017] [Accepted: 05/12/2017] [Indexed: 10/19/2022]
Abstract
This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.
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Affiliation(s)
- É O Rodrigues
- Department of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
| | - L O Rodrigues
- School of Pharmacy, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
| | - L S N Oliveira
- School of Nursing, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
| | - A Conci
- Department of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil.
| | - P Liatsis
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Petroleum Institute, PO Box 2533, Abu Dhabi, United Arab Emirates.
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