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Background: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19
patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung
images of such patients. Two structurally-different deep learning techniques, SegNet and UNET, are
investigated for semantically segmenting infected tissue regions in CT lung images.
Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissue
classification. SegNet is characterized as as scene segmentation network and UNET as a medical segmentation
tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung
tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using
seventy-two data images, validated on ten images, and tested against the left eighteen images. Several
statistical scores are calculated for the results and tabulated accordingly.
Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared
to the other methods (with 0:95 mean accuracy), while the UNET shows better results as a multi-class
segmentor (with 0:91 mean accuracy).
Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would
not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the
population treatment accordingly. We propose computer-based techniques that prove to be reliable as
detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would
help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
Keywords
COVID-19, pneumonia, SegNet, UNET, Computerized Tomography, Semantic Segmentation
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CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
No community comments so far
Editor assigned
On 22 Nov, 2020
Editorial decision: Accept
On 22 Nov, 2020
Submission checks complete
On 22 Nov, 2020
Editor invited
On 22 Nov, 2020
Version 2
Posted 30 Oct, 2020
No comments provided
Review #2 received
Received 18 Nov, 2020
Editorial decision: Minor revision
On 18 Nov, 2020
Review #1 received
Received 07 Nov, 2020
Reviewer #2 agreed
On 30 Oct, 2020
Reviewer #1 agreed
On 28 Oct, 2020
Editor assigned
On 26 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Submission checks complete
On 25 Oct, 2020
Editor invited
On 25 Oct, 2020
No comments provided
Review #1 received
Received 23 Sep, 2020
Editorial decision: Major revision
On 23 Sep, 2020
Review #2 received
Received 21 Sep, 2020
Reviewer #3 agreed
On 11 Sep, 2020
Reviewer #2 agreed
On 10 Sep, 2020
Reviewer #1 agreed
On 03 Sep, 2020
Editor assigned
On 28 Aug, 2020
Reviewers invited
Invitations sent on 28 Aug, 2020
Submission checks complete
On 27 Aug, 2020
Editor invited
On 27 Aug, 2020
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Subject Areas
Nuclear Medicine & Medical Imaging
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Medical Imaging.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Technical advance
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
No community comments so far
Editor assigned
On 22 Nov, 2020
Editorial decision: Accept
On 22 Nov, 2020
Submission checks complete
On 22 Nov, 2020
Editor invited
On 22 Nov, 2020
Version 2
Posted 30 Oct, 2020
No comments provided
Review #2 received
Received 18 Nov, 2020
Editorial decision: Minor revision
On 18 Nov, 2020
Review #1 received
Received 07 Nov, 2020
Reviewer #2 agreed
On 30 Oct, 2020
Reviewer #1 agreed
On 28 Oct, 2020
Editor assigned
On 26 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Submission checks complete
On 25 Oct, 2020
Editor invited
On 25 Oct, 2020
No comments provided
Review #1 received
Received 23 Sep, 2020
Editorial decision: Major revision
On 23 Sep, 2020
Review #2 received
Received 21 Sep, 2020
Reviewer #3 agreed
On 11 Sep, 2020
Reviewer #2 agreed
On 10 Sep, 2020
Reviewer #1 agreed
On 03 Sep, 2020
Editor assigned
On 28 Aug, 2020
Reviewers invited
Invitations sent on 28 Aug, 2020
Submission checks complete
On 27 Aug, 2020
Editor invited
On 27 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Imaging.
Background: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19
patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung
images of such patients. Two structurally-different deep learning techniques, SegNet and UNET, are
investigated for semantically segmenting infected tissue regions in CT lung images.
Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissue
classification. SegNet is characterized as as scene segmentation network and UNET as a medical segmentation
tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung
tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using
seventy-two data images, validated on ten images, and tested against the left eighteen images. Several
statistical scores are calculated for the results and tabulated accordingly.
Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared
to the other methods (with 0:95 mean accuracy), while the UNET shows better results as a multi-class
segmentor (with 0:91 mean accuracy).
Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would
not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the
population treatment accordingly. We propose computer-based techniques that prove to be reliable as
detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would
help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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