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Tamal M, Althobaiti M, Alhashim M, Alsanea M, Hegazi TM, Deriche M, Alhashem AM. Radiomic features based automatic classification of CT lung findings for COVID-19 patients. Biomed Phys Eng Express 2024; 11:015012. [PMID: 39530647 DOI: 10.1088/2057-1976/ad9157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Accepted: 11/12/2024] [Indexed: 11/16/2024]
Abstract
Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.
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Affiliation(s)
- Mahbubunnabi Tamal
- Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Murad Althobaiti
- Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Maryam Alhashim
- Department of medical physics, King Fahad Specialist Hospital Dammam, Dammam 32253, Saudi Arabia
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam 31441, Saudi Arabia
| | - Maram Alsanea
- Department of medical physics, King Fahad Specialist Hospital Dammam, Dammam 32253, Saudi Arabia
| | - Tarek M Hegazi
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam 31441, Saudi Arabia
| | - Mohamed Deriche
- Artificial Intelligence Research Centre, AIRC, Ajman University, United Arab Emirates
| | - Abdullah M Alhashem
- Neuroradiology Consultant, Radiology Department, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
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2
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Cè M, Chiriac MD, Cozzi A, Macrì L, Rabaiotti FL, Irmici G, Fazzini D, Carrafiello G, Cellina M. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies. Diagnostics (Basel) 2024; 14:2473. [PMID: 39594139 PMCID: PMC11593328 DOI: 10.3390/diagnostics14222473] [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: 09/04/2024] [Revised: 10/25/2024] [Accepted: 10/28/2024] [Indexed: 11/28/2024] Open
Abstract
Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)-a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | | | - Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| | - Laura Macrì
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Francesca Lucrezia Rabaiotti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Breast Imaging Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian 1, 20133 Milan, Italy
| | - Deborah Fazzini
- Radiology Department, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy
- Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy
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Schouten G, Michielsen BSHT, Gravendeel B. Data-centric AI approach for automated wildflower monitoring. PLoS One 2024; 19:e0302958. [PMID: 39250497 PMCID: PMC11383241 DOI: 10.1371/journal.pone.0302958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 08/19/2024] [Indexed: 09/11/2024] Open
Abstract
We present the Eindhoven Wildflower Dataset (EWD) as well as a PyTorch object detection model that is able to classify and count wildflowers. EWD, collected over two entire flowering seasons and expert annotated, contains 2,002 top-view images of flowering plants captured 'in the wild' in five different landscape types (roadsides, urban green spaces, cropland, weed-rich grassland, marshland). It holds a total of 65,571 annotations for 160 species belonging to 31 different families of flowering plants and serves as a reference dataset for automating wildflower monitoring and object detection in general. To ensure consistent annotations, we define species-specific floral count units and provide extensive annotation guidelines. With a 0.82 mAP (@IoU > 0.50) score the presented baseline model, trained on a balanced subset of EWD, is to the best of our knowledge superior in its class. Our approach empowers automated quantification of wildflower richness and abundance, which helps understanding and assessing natural capital, and encourages the development of standards for AI-based wildflower monitoring. The annotated EWD dataset and the code to train and run the baseline model are publicly available.
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Affiliation(s)
- Gerard Schouten
- School of ICT, Fontys University of Applied Sciences, Eindhoven, Netherlands
- Naturalis Biodiversity Center, Leiden, Netherlands
| | - Bas S H T Michielsen
- School of ICT, Fontys University of Applied Sciences, Eindhoven, Netherlands
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | - Barbara Gravendeel
- Naturalis Biodiversity Center, Leiden, Netherlands
- Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, Netherlands
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Zhang Z, Li G, Wang Z, Xia F, Zhao N, Nie H, Ye Z, Lin JS, Hui Y, Liu X. Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT. Sci Rep 2024; 14:11987. [PMID: 38796521 PMCID: PMC11127985 DOI: 10.1038/s41598-024-62887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/22/2024] [Indexed: 05/28/2024] Open
Abstract
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.
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Affiliation(s)
- Zhongyi Zhang
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China
| | - Guixia Li
- Department of Nephrology, Shenzhen Third People's Hospital, the Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518112, Guangdong, China
| | - Ziqiang Wang
- Department of Nephrology, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102, Hainan, China
| | - Feng Xia
- Department of Cardiovascular Surgery, Wuhan Asia General Hospital, Wuhan, 430000, Hubei, China
| | - Ning Zhao
- The First Clinical Medical School, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Huibin Nie
- Department of Nephrology, Chengdu First People's Hospital, Chengdu, 610021, Sichuan, China
| | - Zezhong Ye
- Independent Researcher, Boston, MA, 02115, USA
| | - Joshua S Lin
- Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Yiyi Hui
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| | - Xiangchun Liu
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China.
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5
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Bumm R, Zaffino P, Lasso A, Estépar RSJ, Pieper S, Wasserthal J, Spadea MF, Latshang T, Kawel-Boehm N, Wäckerlin A, Werner R, Hässig G, Furrer M, Kikinis R. Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients. J Thorac Dis 2024; 16:1009-1020. [PMID: 38505008 PMCID: PMC10944742 DOI: 10.21037/jtd-23-1150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/15/2023] [Indexed: 03/21/2024]
Abstract
Background The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments. Methods A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
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Affiliation(s)
- Rudolf Bumm
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen’s University, Kingston, Canada
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jakob Wasserthal
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Maria Francesca Spadea
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Tsogyal Latshang
- Department of Pneumonology, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Nadine Kawel-Boehm
- Department of Radiology, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Adrian Wäckerlin
- Department of Intensive Care Medicine, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Raphael Werner
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Gabriela Hässig
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Markus Furrer
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Ron Kikinis
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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6
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Abedi I, Vali M, Otroshi B, Zamanian M, Bolhasani H. HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation. BMC Res Notes 2024; 17:32. [PMID: 38254225 PMCID: PMC10804784 DOI: 10.1186/s13104-024-06693-z] [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: 10/28/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
INTRODUCTION Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. DATA DESCRIPTION To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.
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Affiliation(s)
- Iraj Abedi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahsa Vali
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Bentolhoda Otroshi
- Department of Radiology, School of Medicine, Arak University of Medical Sciences, Arak, Iran
| | - Maryam Zamanian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hamidreza Bolhasani
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
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Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel) 2023; 10:1435. [PMID: 38136026 PMCID: PMC10740686 DOI: 10.3390/bioengineering10121435] [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: 11/18/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
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Affiliation(s)
- Luís Pinto-Coelho
- ISEP—School of Engineering, Polytechnic Institute of Porto, 4200-465 Porto, Portugal;
- INESCTEC, Campus of the Engineering Faculty of the University of Porto, 4200-465 Porto, Portugal
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Thanoon MA, Zulkifley MA, Mohd Zainuri MAA, Abdani SR. A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images. Diagnostics (Basel) 2023; 13:2617. [PMID: 37627876 PMCID: PMC10453592 DOI: 10.3390/diagnostics13162617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023] Open
Abstract
One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient's probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.
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Affiliation(s)
- Mohammad A. Thanoon
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
- System and Control Engineering Department, College of Electronics Engineering, Ninevah University, Mosul 41002, Iraq
| | - Mohd Asyraf Zulkifley
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
| | - Muhammad Ammirrul Atiqi Mohd Zainuri
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
| | - Siti Raihanah Abdani
- School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Shah Alam 40450, Malaysia;
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Bumm R, Zaffino P, Lasso A, Estépar RSJ, Pieper S, Wasserthal J, Spadea MF, Latshang T, Kawel-Böhm N, Wäckerlin A, Werner R, Hässig G, Furrer M, Kikinis R. From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases. RESEARCH SQUARE 2023:rs.3.rs-3027617. [PMID: 37333197 PMCID: PMC10275043 DOI: 10.21203/rs.3.rs-3027617/v5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Background The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts. Methods A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (p < 0.05) No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves of the lungs and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
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Affiliation(s)
- Rudolf Bumm
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, 88100 Catanzaro, Italy
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jakob Wasserthal
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Maria Francesca Spadea
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Tsogyal Latshang
- Department of Pneumonology, Kantonsspital Graubünden, Chur, Switzerland
| | - Nadine Kawel-Böhm
- Department of Radiology, Kantonsspital Graubünden, Chur, Switzerland
| | - Adrian Wäckerlin
- Department of Intensive Care Medicine, Kantonsspital Graubünden, Chur, Switzerland
| | - Raphael Werner
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Gabriela Hässig
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Markus Furrer
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Gao C, Killeen BD, Hu Y, Grupp RB, Taylor RH, Armand M, Unberath M. Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis. NAT MACH INTELL 2023; 5:294-308. [PMID: 38523605 PMCID: PMC10959504 DOI: 10.1038/s42256-023-00629-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 02/06/2023] [Indexed: 03/26/2024]
Abstract
Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.
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Affiliation(s)
- Cong Gao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Benjamin D. Killeen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yicheng Hu
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Robert B. Grupp
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Russell H. Taylor
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mehran Armand
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Orthopaedic Surgery, Johns Hopkins Applied Physics Laboratory, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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Deep Learning for Detecting COVID-19 Using Medical Images. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010019. [PMID: 36671590 PMCID: PMC9854504 DOI: 10.3390/bioengineering10010019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
The global spread of COVID-19 (also known as SARS-CoV-2) is a major international public health crisis [...].
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12
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Zhang Z, Li Y, Shin BS. Robust Medical Image Colorization with Spatial Mask-Guided Generative Adversarial Network. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120721. [PMID: 36550927 PMCID: PMC9774564 DOI: 10.3390/bioengineering9120721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022]
Abstract
Color medical images provide better visualization and diagnostic information for doctors during clinical procedures than grayscale medical images. Although generative adversarial network-based image colorization approaches have shown promising results, in these methods, adversarial training is applied to the whole image without considering the appearance conflicts between the foreground objects and the background contents, resulting in generating various artifacts. To remedy this issue, we propose a fully automatic spatial mask-guided colorization with generative adversarial network (SMCGAN) framework for medical image colorization. It generates colorized images with fewer artifacts by introducing spatial masks, which encourage the network to focus on the colorization of the foreground regions instead of the whole image. Specifically, we propose a novel spatial mask-guided method by introducing an auxiliary foreground segmentation branch combined with the main colorization branch to obtain the spatial masks. The spatial masks are then used to generate masked colorized images where most background contents are filtered out. Moreover, two discriminators are utilized for the generated colorized images and masked generated colorized images, respectively, to assist the model in focusing on the colorization of foreground regions. We validate our proposed framework on two publicly available datasets, including the Visible Human Project (VHP) dataset and the prostate dataset from NCI-ISBI 2013 challenge. The experimental results demonstrate that SMCGAN outperforms the state-of-the-art GAN-based image colorization approaches with an average improvement of 8.48% in the PSNR metric. The proposed SMCGAN can also generate colorized medical images with fewer artifacts.
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13
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Sherwani MK, Marzullo A, De Momi E, Calimeri F. Lesion segmentation in lung CT scans using unsupervised adversarial learning. Med Biol Eng Comput 2022; 60:3203-3215. [PMID: 36125656 PMCID: PMC9486778 DOI: 10.1007/s11517-022-02651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 07/28/2022] [Indexed: 12/01/2022]
Abstract
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.
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Affiliation(s)
- Moiz Khan Sherwani
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy.
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
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Alsaaidah B, Al-Hadidi MR, Al-Nsour H, Masadeh R, AlZubi N. Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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Affiliation(s)
- Bayan Alsaaidah
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Moh’d Rasoul Al-Hadidi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
| | - Heba Al-Nsour
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Raja Masadeh
- Computer Science Department, The World Islamic Sciences and Education University, Amman 11947, Jordan
| | - Nael AlZubi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
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Ali S, Zhou Y, Patterson M. Efficient analysis of COVID-19 clinical data using machine learning models. Med Biol Eng Comput 2022; 60:1881-1896. [PMID: 35507111 PMCID: PMC9066140 DOI: 10.1007/s11517-022-02570-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/27/2022] [Indexed: 11/29/2022]
Abstract
Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before by leveraging such big data. This data is of many different varieties and can be of different levels of veracity, e.g., precise, imprecise, uncertain, and missing, making it challenging to extract meaningful information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform - often in real-time - the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning-based algorithms to this big data is a natural approach to take to this aim since they can quickly scale to such data and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19 and potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation and then propose a model that first performs efficient feature selection from such representation. We apply this approach to two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policymakers focus on only certain attributes to study this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.
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Affiliation(s)
- Sarwan Ali
- Georgia State University, Atlanta, GA USA
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16
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Hassan H, Ren Z, Zhao H, Huang S, Li D, Xiang S, Kang Y, Chen S, Huang B. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comput Biol Med 2022; 141:105123. [PMID: 34953356 PMCID: PMC8684223 DOI: 10.1016/j.compbiomed.2021.105123] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 01/12/2023]
Abstract
This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.
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Affiliation(s)
- Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
| | - Zhaoyu Ren
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Huishi Zhao
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shoujin Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Dan Li
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shaohua Xiang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Yan Kang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; Medical Device Innovation Research Center, Shenzhen Technology University, Shenzhen, China
| | - Sifan Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
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Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics. ELECTRONICS 2021. [DOI: 10.3390/electronics10202475] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.
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Singh G, Yow KC. Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images. Diagnostics (Basel) 2021; 11:1732. [PMID: 34574073 PMCID: PMC8465137 DOI: 10.3390/diagnostics11091732] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
The new strains of the pandemic COVID-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of COVID-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect COVID-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%.
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Affiliation(s)
| | - Kin-Choong Yow
- Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada;
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Stefano A, Comelli A. Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images. J Imaging 2021; 7:131. [PMID: 34460767 PMCID: PMC8404925 DOI: 10.3390/jimaging7080131] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/28/2021] [Accepted: 08/01/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. METHODS In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. RESULTS The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. CONCLUSIONS We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.
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Affiliation(s)
- Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
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