1
|
Saha S, Dutta S, Goswami B, Nandi D. ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images. Biomed Signal Process Control 2023; 85:104974. [PMID: 37122956 PMCID: PMC10121143 DOI: 10.1016/j.bspc.2023.104974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 04/01/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
An automatic method for qualitative and quantitative evaluation of chest Computed Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop an automated COVID-19 prediction framework using deep learning. We put forth a novel Deep Neural Network (DNN) composed of an attention-based dense U-Net with deep supervision for COVID-19 lung lesion segmentation from chest CT images. We incorporate dense U-Net where convolution kernel size 5×5 is used instead of 3×3. The dense and transition blocks are introduced to implement a densely connected network on each encoder level. Also, the attention mechanism is applied between the encoder, skip connection, and decoder. These are used to keep both the high and low-level features efficiently. The deep supervision mechanism creates secondary segmentation maps from the features. Deep supervision combines secondary supervision maps from various resolution levels and produces a better final segmentation map. The trained artificial DNN model takes the test data at its input and generates a prediction output for COVID-19 lesion segmentation. The proposed model has been applied to the MedSeg COVID-19 chest CT segmentation dataset. Data pre-processing methods help the training process and improve performance. We compare the performance of the proposed DNN model with state-of-the-art models by computing the well-known metrics: dice coefficient, Jaccard coefficient, accuracy, specificity, sensitivity, and precision. As a result, the proposed model outperforms the state-of-the-art models. This new model may be considered an efficient automated screening system for COVID-19 diagnosis and can potentially improve patient health care and management system.
Collapse
Affiliation(s)
- Sanjib Saha
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, 713209, West Bengal, India
- Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, 713206, West Bengal, India
| | - Subhadeep Dutta
- Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, 713206, West Bengal, India
| | - Biswarup Goswami
- Department of Respiratory Medicine, Health and Family Welfare, Government of West Bengal, Kolkata, 700091, West Bengal, India
| | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, 713209, West Bengal, India
| |
Collapse
|
2
|
Wang L, Zhou H, Xu N, Liu Y, Jiang X, Li S, Feng C, Xu H, Deng K, Song J. A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience 2023; 26:107005. [PMID: 37534183 PMCID: PMC10391673 DOI: 10.1016/j.isci.2023.107005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/27/2023] [Accepted: 05/26/2023] [Indexed: 08/04/2023] Open
Abstract
Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.
Collapse
Affiliation(s)
- Lu Wang
- Department of Library, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - He Zhou
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Nan Xu
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Yuchan Liu
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Shenyang, Liaoning 110169, China
| | - Hainan Xu
- Department of Obstetrics and Gynecology, Pelvic Floor Disease Diagnosis and Treatment Center, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| |
Collapse
|
3
|
Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
- Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan
| | - Mahrukh Saif
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Asiya Batool
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan
| | - Muhammad Waleed Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Mechanical and Aerospace Engineering, Columbus, OH, USA
| |
Collapse
|
4
|
He T, Liu H, Zhang Z, Li C, Zhou Y. Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1158. [PMID: 36673913 PMCID: PMC9858906 DOI: 10.3390/ijerph20021158] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/26/2022] [Accepted: 01/02/2023] [Indexed: 05/31/2023]
Abstract
Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.
Collapse
Affiliation(s)
- Tiancheng He
- Department of Political Party and State Governance, East China University of Political Science and Law, Shanghai 201620, China
| | - Hong Liu
- Department of Political Party and State Governance, East China University of Political Science and Law, Shanghai 201620, China
- Teacher Work Department of the Party Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhihao Zhang
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
| | - Chao Li
- Department of Computer Science, Zhijiang College of Zhejiang University of Technology, Hangzhou 310024, China
| | - Youmei Zhou
- Department of Landscape Architecture, College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
| |
Collapse
|
5
|
Chakraborty S, Mali K. SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation. Appl Soft Comput 2022; 129:109625. [PMID: 36124000 PMCID: PMC9474408 DOI: 10.1016/j.asoc.2022.109625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/27/2022]
Abstract
COVID-19 causes an ongoing worldwide pandemic situation. The non-discovery of specialized drugs and/or any other kind of medicines makes the situation worse. Early diagnosis of this disease will be certainly helpful to start the treatment early and also to bring down the dire spread of this highly infectious virus. This article describes the proposed novel unsupervised segmentation method to segment the radiological image samples of the chest area that are accumulated from the COVID-19 infected patients. The proposed approach is helpful for physicians, medical technologists, and other related experts in the quick and early diagnosis of COVID-19 infection. The proposed approach will be the SUFEMO (SUperpixel based Fuzzy Electromagnetism-like Optimization). This approach is developed depending on some well-known theories like the Electromagnetism-like optimization algorithm, the type-2 fuzzy logic, and the superpixels. The proposed approach brings down the processing burden that is required to deal with a considerably large amount of spatial information by assimilating the notion of the superpixel. In this work, the EMO approach is modified by utilizing the type 2 fuzzy framework. The EMO approach updates the cluster centers without using the cluster center updation equation. This approach is independent of the choice of the initial cluster centers. To decrease the related computational overhead of handling a lot of spatial data, a novel superpixel-based approach is proposed in which the noise-sensitiveness of the watershed-based superpixel formation approach is dealt with by computing the nearby minima from the gradient image. Also, to take advantage of the superpixels, the fuzzy objective function is modified. The proposed approach was evaluated using both qualitatively and quantitatively using 310 chest CT scan images that are gathered from various sources. Four standard cluster validity indices are taken into consideration to quantify the results. It is observed that the proposed approach gives better performance compared to some of the state-of-the-art approaches in terms of both qualitative and quantitative outcomes. On average, the proposed approach attains Davies-Bouldin index value of 1.812008792, Xie-Beni index value of 1.683281, Dunn index value 2.588595748, and β index value 3.142069236 for 5 clusters. Apart from this, the proposed approach is also found to be superior with regard to the rate of convergence. Rigorous experiments prove the effectiveness of the proposed approach and establish the real-life applicability of the proposed method for the initial filtering of the COVID-19 patients.
Collapse
Affiliation(s)
- Shouvik Chakraborty
- Department of Computer Science and Engineering, University of Kalyani, India
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, India
| |
Collapse
|
6
|
Owais M, Baek NR, Park KR. DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans. EXPERT SYSTEMS WITH APPLICATIONS 2022; 202:117360. [PMID: 35529253 PMCID: PMC9057951 DOI: 10.1016/j.eswa.2022.117360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/24/2022] [Accepted: 04/25/2022] [Indexed: 05/14/2023]
Abstract
The recent disaster of COVID-19 has brought the whole world to the verge of devastation because of its highly transmissible nature. In this pandemic, radiographic imaging modalities, particularly, computed tomography (CT), have shown remarkable performance for the effective diagnosis of this virus. However, the diagnostic assessment of CT data is a human-dependent process that requires sufficient time by expert radiologists. Recent developments in artificial intelligence have substituted several personal diagnostic procedures with computer-aided diagnosis (CAD) methods that can make an effective diagnosis, even in real time. In response to COVID-19, various CAD methods have been developed in the literature, which can detect and localize infectious regions in chest CT images. However, most existing methods do not provide cross-data analysis, which is an essential measure for assessing the generality of a CAD method. A few studies have performed cross-data analysis in their methods. Nevertheless, these methods show limited results in real-world scenarios without addressing generality issues. Therefore, in this study, we attempt to address generality issues and propose a deep learning-based CAD solution for the diagnosis of COVID-19 lesions from chest CT images. We propose a dual multiscale dilated fusion network (DMDF-Net) for the robust segmentation of small lesions in a given CT image. The proposed network mainly utilizes the strength of multiscale deep features fusion inside the encoder and decoder modules in a mutually beneficial manner to achieve superior segmentation performance. Additional pre- and post-processing steps are introduced in the proposed method to address the generality issues and further improve the diagnostic performance. Mainly, the concept of post-region of interest (ROI) fusion is introduced in the post-processing step, which reduces the number of false-positives and provides a way to accurately quantify the infected area of lung. Consequently, the proposed framework outperforms various state-of-the-art methods by accomplishing superior infection segmentation results with an average Dice similarity coefficient of 75.7%, Intersection over Union of 67.22%, Average Precision of 69.92%, Sensitivity of 72.78%, Specificity of 99.79%, Enhance-Alignment Measure of 91.11%, and Mean Absolute Error of 0.026.
Collapse
Affiliation(s)
- Muhammad Owais
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
| | - Na Rae Baek
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
| | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
| |
Collapse
|
7
|
Wu X, Zhang Z, Guo L, Chen H, Luo Q, Jin B, Gu W, Lu F, Chen J. FAM: focal attention module for lesion segmentation of COVID-19 CT images. JOURNAL OF REAL-TIME IMAGE PROCESSING 2022; 19:1091-1104. [PMID: 36091622 PMCID: PMC9441194 DOI: 10.1007/s11554-022-01249-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/12/2022] [Indexed: 05/27/2023]
Abstract
The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).
Collapse
Affiliation(s)
- Xiaoxin Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Zhihao Zhang
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
| | - Lingling Guo
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang China
| | - Hui Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Qiaojie Luo
- School of Stomatology, Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Bei Jin
- Department of Oral and Maxillofacial Surgery, Taizhou Hospital, Wenzhou Medical University, Taizhou, Zhejiang China
| | - Weiyan Gu
- Department of Oral and Maxillofacial Surgery, Taizhou Hospital, Wenzhou Medical University, Taizhou, Zhejiang China
| | - Fangfang Lu
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
| | - Jingjing Chen
- Zhejiang University City College, Hangzhou, Zhejiang China
| |
Collapse
|
8
|
Connell M, Xin Y, Gerard SE, Herrmann J, Shah PK, Martin KT, Rezoagli E, Ippolito D, Rajaei J, Baron R, Delvecchio P, Humayun S, Rizi RR, Bellani G, Cereda M. Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN. Methods 2022; 205:200-209. [PMID: 35817338 PMCID: PMC9288584 DOI: 10.1016/j.ymeth.2022.07.007] [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: 11/05/2021] [Revised: 05/18/2022] [Accepted: 07/06/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data. METHODS We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists. RESULTS The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations. CONCLUSION Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.
Collapse
Affiliation(s)
- Marc Connell
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi Xin
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah E Gerard
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jacob Herrmann
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Parth K Shah
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin T Martin
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emanuele Rezoagli
- Department of Emergency and Intensive Care, San Gerardo Hospital, Monza, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Davide Ippolito
- Department of Diagnostic and Interventional Radiology, San Gerardo Hospital, Monza, Italy
| | - Jennia Rajaei
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ryan Baron
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paolo Delvecchio
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Shiraz Humayun
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Rahim R Rizi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Giacomo Bellani
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maurizio Cereda
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
9
|
Alshayeji MH, ChandraBhasi Sindhu S, Abed S. CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images. BMC Bioinformatics 2022; 23:264. [PMID: 35794537 PMCID: PMC9261058 DOI: 10.1186/s12859-022-04818-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Abstract
Background Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework. Results The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25–50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50. Conclusions The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.
Collapse
Affiliation(s)
- Mohammad H Alshayeji
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait.
| | | | - Sa'ed Abed
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait
| |
Collapse
|
10
|
Varriano G, Guerriero P, Santone A, Mercaldo F, Brunese L. Explainability of radiomics through formal methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106824. [PMID: 35483269 DOI: 10.1016/j.cmpb.2022.106824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems. METHODS Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences. RESULTS In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach. CONCLUSIONS In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.
Collapse
Affiliation(s)
- Giulia Varriano
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Pasquale Guerriero
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Antonella Santone
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| | - Luca Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy.
| |
Collapse
|
11
|
Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 1.0 Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans. Diagnostics (Basel) 2022; 12:diagnostics12051283. [PMID: 35626438 PMCID: PMC9141749 DOI: 10.3390/diagnostics12051283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.
Collapse
Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Armin Mehmedović
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95661, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Klaudija Viskovic
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
| |
Collapse
|
12
|
Fang C, Liu Y, Liu Y, Liu M, Qiu X, Li Y, Wen J, Yang Y. Label-Free Covid-19 lesion segmentation based on synthetic healthy lung image subtraction. Med Phys 2022; 49:4632-4641. [PMID: 35397134 PMCID: PMC9088629 DOI: 10.1002/mp.15661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 01/06/2022] [Accepted: 03/07/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised approach using cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation. METHOD The workflow includes lung volume segmentation, healthy lung image synthesis, infected and healthy image subtraction, and binary lesion mask creation. The lung volume was first delineated using a pre-trained U-net and worked as the input for the following network. A cycle-GAN was developed to generate synthetic healthy lung CT images from infected lung images. After that, the pneumonia lesions are extracted by subtracting the synthetic healthy lung CT images from the infected lung CT images. A median filter and K-means clustering were then applied to contour the lesions. The auto segmentation approach was validated on three different datasets. RESULTS The average Dice coefficient reached 0.666±0.178 on the three datasets. Especially, the dice reached 0.748±0.121 and 0.730±0.095, respectively, on two public datasets Coronacases and Radiopedia. Meanwhile, the average precision and sensitivity for lesion segmentation on the three datasets were 0.679±0.244 and 0.756±0.162. The performance is comparable to existing supervised segmentation networks and outperforms unsupervised ones. CONCLUSION The proposed label-free segmentation method achieved high accuracy and efficiency in automatic COVID-19 lesion delineation. The segmentation result can serve as a baseline for further manual modification and a quality assurance tool for lesion diagnosis. Furthermore, due to its unsupervised nature, the result is not influenced by physicians' experience which otherwise is crucial for supervised methods. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Chengyijue Fang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Yingao Liu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Ying Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Mengqiu Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Xiaohui Qiu
- Department of Radiology, Bozhou People's Hospital, Bozhou, Anhui, 236800, China
| | - Yang Li
- Department of Radiology, the First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233000, China
| | - Jie Wen
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Yidong Yang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China.,Department of Radiation Oncology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| |
Collapse
|
13
|
Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
| |
Collapse
|
14
|
Owais M, Baek NR, Park KR. Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans. J Pers Med 2021; 11:1008. [PMID: 34683149 PMCID: PMC8537687 DOI: 10.3390/jpm11101008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists. METHOD A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients). RESULTS Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods. CONCLUSIONS These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.
Collapse
Affiliation(s)
| | | | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; (M.O.); (N.R.B.)
| |
Collapse
|
15
|
Zhang Q, Chen Z, Liu G, Zhang W, Du Q, Tan J, Gao Q. Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images. Infect Drug Resist 2021; 14:671-687. [PMID: 33658806 PMCID: PMC7917359 DOI: 10.2147/idr.s296346] [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: 12/08/2020] [Accepted: 01/21/2021] [Indexed: 12/31/2022] Open
Abstract
Purpose Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images. Methods We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model. Results This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability. Conclusion The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.
Collapse
Affiliation(s)
- Quan Zhang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| | - Zhuo Chen
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China
| | - Guohua Liu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| | - Wenjia Zhang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| | - Qian Du
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| | - Jiayuan Tan
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| | - Qianqian Gao
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People's Republic of China
| |
Collapse
|
16
|
Santone A, Belfiore MP, Mercaldo F, Varriano G, Brunese L. On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis. Diagnostics (Basel) 2021; 11:293. [PMID: 33673394 PMCID: PMC7917767 DOI: 10.3390/diagnostics11020293] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 01/29/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022] Open
Abstract
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.
Collapse
Affiliation(s)
- Antonella Santone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Napoli, Italy;
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Giulia Varriano
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Luca Brunese
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| |
Collapse
|
17
|
Wan Y, Zhou H, Zhang X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2021; 23:204. [PMID: 33562309 PMCID: PMC7916048 DOI: 10.3390/e23020204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 12/15/2022]
Abstract
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.
Collapse
Affiliation(s)
- Yuchai Wan
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
| | | | | |
Collapse
|