CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans
Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/ups...
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| Published in: | IEEE transactions on artificial intelligence Vol. 2; no. 3; pp. 283 - 297 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2021
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| ISSN: | 2691-4581, 2691-4581 |
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| Abstract | Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in suboptimal performance. Moreover, operating with 3-D CT volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this article, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2-D network is employed for generating region-of-interest (ROI)-enhanced CT volume followed by a shallower 3-D network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multistage encoder-decoder modules for achieving optimum performance. Additionally, multiscale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multiscale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications.
Impact Statement -With lower sensitivity (60-70%), elongated testing time, and a dire shortage of testing kits, traditional RTPCR based COVID-19 diagnostic scheme heavily relies on postCT based manual inspection for further investigation. Hence, automating the process of infected lesions extraction from chestCT volumes will be major progress for faster accurate diagnosis of COVID-19. However, in challenging conditions with diffused, blurred, and varying shaped edges of COVID-19 lesions, conventional approaches fail to provide precise segmentation of lesions that can be deleterious for false estimation and loss of information. The proposed scheme incorporating an efficient neural network architecture (CovSegNet) overcomes the limitations of traditional approaches that provide significant improvement of performance (8.4% in averaged dice measurement scale) over two datasets. Therefore, this scheme can be an effective, economical tool for the physicians for faster infection analysis to greatly reduce the spread and massive death toll of this deadly virus through mass-screening. |
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| AbstractList | Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in suboptimal performance. Moreover, operating with 3-D CT volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this article, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2-D network is employed for generating region-of-interest (ROI)-enhanced CT volume followed by a shallower 3-D network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multistage encoder-decoder modules for achieving optimum performance. Additionally, multiscale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multiscale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications. Impact Statement-With lower sensitivity (60-70%), elongated testing time, and a dire shortage of testing kits, traditional RTPCR based COVID-19 diagnostic scheme heavily relies on postCT based manual inspection for further investigation. Hence, automating the process of infected lesions extraction from chestCT volumes will be major progress for faster accurate diagnosis of COVID-19. However, in challenging conditions with diffused, blurred, and varying shaped edges of COVID-19 lesions, conventional approaches fail to provide precise segmentation of lesions that can be deleterious for false estimation and loss of information. The proposed scheme incorporating an efficient neural network architecture (CovSegNet) overcomes the limitations of traditional approaches that provide significant improvement of performance (8.4% in averaged dice measurement scale) over two datasets. Therefore, this scheme can be an effective, economical tool for the physicians for faster infection analysis to greatly reduce the spread and massive death toll of this deadly virus through mass-screening.Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in suboptimal performance. Moreover, operating with 3-D CT volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this article, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2-D network is employed for generating region-of-interest (ROI)-enhanced CT volume followed by a shallower 3-D network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multistage encoder-decoder modules for achieving optimum performance. Additionally, multiscale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multiscale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications. Impact Statement-With lower sensitivity (60-70%), elongated testing time, and a dire shortage of testing kits, traditional RTPCR based COVID-19 diagnostic scheme heavily relies on postCT based manual inspection for further investigation. Hence, automating the process of infected lesions extraction from chestCT volumes will be major progress for faster accurate diagnosis of COVID-19. However, in challenging conditions with diffused, blurred, and varying shaped edges of COVID-19 lesions, conventional approaches fail to provide precise segmentation of lesions that can be deleterious for false estimation and loss of information. The proposed scheme incorporating an efficient neural network architecture (CovSegNet) overcomes the limitations of traditional approaches that provide significant improvement of performance (8.4% in averaged dice measurement scale) over two datasets. Therefore, this scheme can be an effective, economical tool for the physicians for faster infection analysis to greatly reduce the spread and massive death toll of this deadly virus through mass-screening. Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in suboptimal performance. Moreover, operating with 3-D CT volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this article, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2-D network is employed for generating region-of-interest (ROI)-enhanced CT volume followed by a shallower 3-D network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multistage encoder-decoder modules for achieving optimum performance. Additionally, multiscale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multiscale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications. Impact Statement -With lower sensitivity (60-70%), elongated testing time, and a dire shortage of testing kits, traditional RTPCR based COVID-19 diagnostic scheme heavily relies on postCT based manual inspection for further investigation. Hence, automating the process of infected lesions extraction from chestCT volumes will be major progress for faster accurate diagnosis of COVID-19. However, in challenging conditions with diffused, blurred, and varying shaped edges of COVID-19 lesions, conventional approaches fail to provide precise segmentation of lesions that can be deleterious for false estimation and loss of information. The proposed scheme incorporating an efficient neural network architecture (CovSegNet) overcomes the limitations of traditional approaches that provide significant improvement of performance (8.4% in averaged dice measurement scale) over two datasets. Therefore, this scheme can be an effective, economical tool for the physicians for faster infection analysis to greatly reduce the spread and massive death toll of this deadly virus through mass-screening. Automatic lung lesion segmentation of chest computer tomography (CT) scans is considered a pivotal stage toward accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder–decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in suboptimal performance. Moreover, operating with 3-D CT volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this article, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2-D network is employed for generating region-of-interest (ROI)-enhanced CT volume followed by a shallower 3-D network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multistage encoder–decoder modules for achieving optimum performance. Additionally, multiscale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multiscale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications. Impact Statement—With lower sensitivity (60–70%), elongated testing time, and a dire shortage of testing kits, traditional RTPCR based COVID-19 diagnostic scheme heavily relies on postCT based manual inspection for further investigation. Hence, automating the process of infected lesions extraction from chestCT volumes will be major progress for faster accurate diagnosis of COVID-19. However, in challenging conditions with diffused, blurred, and varying shaped edges of COVID-19 lesions, conventional approaches fail to provide precise segmentation of lesions that can be deleterious for false estimation and loss of information. The proposed scheme incorporating an efficient neural network architecture (CovSegNet) overcomes the limitations of traditional approaches that provide significant improvement of performance (8.4% in averaged dice measurement scale) over two datasets. Therefore, this scheme can be an effective, economical tool for the physicians for faster infection analysis to greatly reduce the spread and massive death toll of this deadly virus through mass-screening. |
| Author | Rahman, Md Awsafur Fattah, Shaikh Anowarul Mahmud, Tanvir Kung, Sun-Yuan |
| AuthorAffiliation | Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology 61750 Dhaka 1000 Bangladesh Department of Electrical Engineering Princeton University 6740 Princeton NJ 08544 USA |
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| Author_xml | – sequence: 1 givenname: Tanvir orcidid: 0000-0003-0529-2826 surname: Mahmud fullname: Mahmud, Tanvir email: tanvirmahmud@eee.buet.ac.bd organization: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 2 givenname: Md Awsafur orcidid: 0000-0003-0966-8835 surname: Rahman fullname: Rahman, Md Awsafur email: mdawsafurrahman@ug.eee.buet.ac.bd organization: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 3 givenname: Shaikh Anowarul orcidid: 0000-0001-8090-2327 surname: Fattah fullname: Fattah, Shaikh Anowarul email: fattah@eee.buet.ac.bd organization: Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 4 givenname: Sun-Yuan orcidid: 0000-0002-7314-0720 surname: Kung fullname: Kung, Sun-Yuan email: kung@princeton.edu organization: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA |
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| Cites_doi | 10.1109/TMI.2020.2992546 10.1016/S0140-6736(20)30185-9 10.1148/radiol.2020200642 10.1016/j.isprsjprs.2020.01.013 10.1016/j.compbiomed.2020.103869 10.46234/ccdcw2020.032 10.1109/TMI.2020.2989737 10.1148/radiol.2020201343 10.1109/TMI.2020.2996645 10.1109/TMI.2019.2950051 10.1109/ISBI.2019.8759329 10.1016/j.neunet.2019.08.025 10.1109/TMI.2020.2975347 10.1109/ACCESS.2019.2896920 10.1007/978-3-319-67389-9_44 10.1109/3DV.2016.79 10.1109/TMI.2019.2959609 10.1148/radiol.2020200330 10.1056/NEJMoa2002032 10.1016/j.dsx.2020.04.012 10.1148/radiol.2020200905 10.1109/TMI.2020.2983721 10.1109/CVPR.2015.7298965 10.1109/TMI.2020.2993291 |
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| SubjectTerms | Artificial intelligence (AI) biomedical imaging Computed tomography computer aided analysis Computer architecture COVID-19 image segmentation Lesions neural networks Optimization Semantics Three-dimensional displays |
| Title | CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans |
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