Estimation of Covid-19 lungs damage based on computer tomography images analysis

Martin Schätz; Olga Rubešová; Jan Mareš; David Girsa; Alan Spark

doi:10.12688/f1000research.109020.1

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Software Tool Article

Estimation of Covid-19 lungs damage based on computer tomography images analysis

[version 1; peer review: 1 approved with reservations, 1 not approved]

Martin Schätz ¹, Olga Rubešová¹, Jan Mareš¹, David Girsa², Alan Spark¹

Martin Schätz ¹, Olga Rubešová¹, [...] Jan Mareš¹, David Girsa², Alan Spark¹

PUBLISHED 17 Mar 2022

Author details Author details

¹ Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic
² Department of Radiodiagnostics 3FM CU and UHKV, Charles University 3rd Faculty of Medicine, Prague, 100 34, Czech Republic

Martin Schätz
Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Olga Rubešová
Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Jan Mareš
Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

David Girsa
Roles: Methodology, Resources

Alan Spark
Roles: Validation

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Coronavirus (COVID-19) collection.

Abstract

Modern treatment is based on reproducible quantitative analysis of available data. The Covid-19 pandemic did accelerate development and research in several multidisciplinary areas. One of them is the use of software tools for faster and reproducible patient data evaluation. A CT scan can be invaluable for a search of details, but it is not always easy to see the big picture in 3D data. Even in the visual analysis of CT slice by slice can inter and intra variability makes a big difference. We present an ImageJ tool developed together with the radiology center of Faculty hospital Královské Vinohrady for CT evaluation of patients with COVID-19. The tool was developed to help estimate the percentage of lungs affected by the infection. The patients can be divided into five groups based on percentage score and proper treatment can be applied

Keywords

Computed Tomography, Image Analysis, ImageJ, Covid-19, Lungs

Corresponding author: Martin Schätz

Competing interests: No competing interests were disclosed.

Grant information: The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.

Copyright: © 2022 Schätz M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Schätz M, Rubešová O, Mareš J et al. Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2022, 11:326 (https://doi.org/10.12688/f1000research.109020.1) First published: 17 Mar 2022, 11:326 (https://doi.org/10.12688/f1000research.109020.1) Latest published: 25 Jul 2025, 11:326 (https://doi.org/10.12688/f1000research.109020.3)

Introduction

The covid pandemic that has affected in recent months has revealed a number of strengths and weaknesses in health systems around the world.

One of the key ideas is a quick and accurate diagnosis of the patient, which was problematic in congested hospitals. Software engineering and image processing methods could be helpful in speeding up and refining patient diagnosis. Especially in radiological and radiodiagnostic workplaces, where a large part of diagnostic processes take place over image data (CT, NMR, X-ray). Various software tools have been used for this purpose for years. In general, it is possible to divide them into two groups:

• universal software packages: used for general analysis of image data such as filtering, smoothing or image registration
• software tools “made to measure”: very specific software tools for analysis of rare diseases

The first group of tools is represented mostly by software integrated into packages supplied by the tomograph developer. It is possible to mention a software tool for CT image preprocessing and automated analysis of three standard phantoms¹ or software tool for metal artifacts reduction in dental care.²

The second group of tools is from both the research and application point of view much more interesting. It is necessary to state that only a small part of them is applied in a real clinical environment. It is possible to mention a tool for analysis of GPA disease using image registration and self-organizing maps,³ or a tool for analysis of peripheral bypass grafts.⁴ Many research groups focused on precise measurement of pathological findings, 3D analysis, or volumetric analysis.⁵^,⁶

Moreover, some papers deal with image fusions from different scanners e.g. combination of data from CT, PET/CT, SPECT/CT, or MR.⁷^,⁸ Thus, the topic of CT image analysis of “covid lungs” is important from both the research point of view (there is still room for further research in precise semi-automatic analysis) and the clinical point of view. Therefore, the aim of this study is to develop a semi-automatic software for “covid lungs” CT image analysis, based on knowledge presented in Ref. 9. The authors present the idea based on the correlation between the degree of lung involvement and the course of the disease. The global score (0–25) of lung involvement is calculated based on the extent of each lobar involvement score (0: 0%, 1: <5%, 2: 5-25%, 3:26 – 50%, 4:51–75%, 5, > 75%). The authors then introduce the role of CT score for predicting the outcome of SARS-CoV-2 patients. The scoring is highly correlated with laboratory findings, disease severity and mortality. Moreover, it might speed up diagnostic workflow in symptomatic cases.

Methods

Image format

The Covid CT estimation tool is based on standard image processing techniques. Our interest is in volume, so the same voxel size is critical for good enough estimation. But it is also important to go through the different types of data we can encounter. In general, the Hounsfield Units (HU) make up the grayscale in medical CT imaging. It is a scale from black to white of 4096 values (12 bit) and ranges from -1024 HU to 3071 HU (zero is also a value). It is defined by the following:

-1024 HU is black and represents air (in the lungs). 0 HU represents water (since we consist mostly out of the water, there is a large peak here). 3071 HU is white and represents the densest tissue in a human body, such as tooth enamel. All other tissues are somewhere within this scale; fat is around -100 HU, muscle around 100 HU, and bone spans from 200 HU (trabecular/spongeous bone) to about 2000 HU (cortical bone).

DICOM files are usually saved in signed 16 bit, with original HU, usually with 3 mm slicing or 0.6 mm slicing CT images. TIFF, however, may have reshaped histogram values to cover the whole range and can preferable be in unsigned 16 bit or 8bit with some loss due to conversion. TIFF values usually lose Z voxel size metadata in conversion (resulting in Z voxel size value of 1), so it is important to reset voxel values. The XY voxel size can be different with each data set, even from the same CT machine. The distribution of intensity values may change with different CT protocols, so some of the processing steps need to be done manually.

Implementation

The workflow follows the Croney Ethical guidelines for the appropriate use and manipulation of scientific digital images.¹⁰

The plugin tool is developed in ImageJ macro language, it needs Bio Format plugin to import DICOM files, which comes installed in FIJI. The macro language uses standard image processing techniques and morphological operations to estimate the volume ratio of lungs and pneumonia caused by COVID-19. It allows users to subsequently set up a threshold for pneumonia and lungs, and go through the whole data-set slice by slice and interactively tweak the threshold values. The tool was developed based on demand and with coordination from the Department of Radiology from the Faculty hospital Královské Vinohrady. It is challenging to do any kind of percentage estimate of pneumonia in the lungs just by visually inspecting CT scans stack by stack. Available hardware equipment and local account restrictions had to be taken into account for development tool selection. The ImageJ plugin is a compromise in accuracy and requirements. The workflow is following:

1. DICOM or Tiff stack is imported, the user is prompted to select sub-volume containing only lungs. This step is necessary for the exclusion of other body cavities, which would be otherwise counted as lungs.
2. User manually thresholds background and inside of lungs (air).
3. A lung mask is created by excluding mask components touching the edge of images and by excluding other objects based on size and roundness.
4. User is prompted to select by threshold all areas containing pneumonia (possible parts outside lungs will be excluded).
5. A clean pneumonia mask is created by element-wise multiplication of lung and pneumonia mask.
6. A volume fraction is estimated by voxel count in each mask.
7. Numeric result and coloured visual representation of original data, lung and pneumonia mask is shown to the user as illustrated on Figure 1.
8. A detailed log with tool version, original folder path, score, percentage results, threshold values, and all masks is saved in a folder next to the original image set. Log contains all information needed to do analysis again with same results.

Figure 1. Result of analysis as RGB stack, where Red channel contains CT data, Green channel lung mask and Blue channel pneumonia mask.

The numeric results in percent is then corrected by subtracting of 3% (median of tissue present in healthy lungs, estimated from 10 patients) and CT is scored based on severity ranged (0:0%; 1, < 5%; 2:5–25%; 3:26–50%; 4:51–75%; 5, > 75%; range 0–5) defined by Ref. 9.

Operation

There are several steps during the tool runtime which require user inputs:

1. Select the CT lung data (Figure 2, TIFF or DICOM file based on the script version) - the CT sequence is opened and user can go through loaded stack in image sequence with a slider or as a video with a play button.
2. “Please find the start of lungs in stack” - user has an option to select the first image with lungs with a slider and confirm the selection with “Ok” button.
3. “Please find the end of lungs in stack” - user has an option to select the last image of lungs selection with the slider and confirm with “Ok” button. The tool works with the images only in between the chosen interval of the lungs stack to minimize the computational effort.
4. “Setup threshold for all but body” - the whole image- exclude the body, shall be highlighted with red colour. The tool makes automatic estimation, and the user can adjust the threshold with the sliders on the histogram. Confirm with the “Ok” button.
5. “Setup threshold of Covid” - the covid threshold shall be highlighted with red colour. The tool makes automatic estimation, and the user can adjust the threshold with the sliders on the histogram. It is not a problem if part of the body (not lungs!) will be chosen together with Covid. The tool automatically subtracts the body threshold from the chosen Covid threshold. Confirm with the “Ok” button.

• After each calculation the tool is adding information to the log window. The log file is automatically saved to the CT data directory. The output lungs and covid masks are saved in TIFF format into an additional folder in the CT data location.
• The tool provides % estimation of Covid damage in the lungs and a semi-quantitative CT score. The score is calculated based on the extent of lobar involvement (0:0%; 1, < 5%; 2:5–25%; 3:26–50%; 4:51–75%; 5, > 75%; range 0–5 based on the medical research “Chest CT score in COVID-19 patients: correlation with the disease severity and short-term prognosis.⁹

Figure 2. ImageJ Tool, loading data options.

The tool has been tested both 3 mm slicing and 0.6 mm slicing CT images. The results were similar in percentage and the final CT score was the same.

In order to use the tool, the user needs to prepare CT images exported as DICOM or TIFF in preferred view mode and preferably 16-bit representation. The CT images have usually a 12-bit gray-scale representation and an 8-bit conversion would lead to loss of potentially important information or shift of brightness values. The thickness of the CT slice can also contribute to numerical errors in the process, but there was no significant difference in results when processing the same data-set with 3 mm and 0.6 slicing.

The ImageJ software tool available from Zenodo needs an ImageJ installed with Bio-Formats (preferably with version 6.8.0 which we tested) plugin (or FIJI which is a version of ImageJ with an already integrated Bio-Formats plugin).

Minimal requirements for both are Windows XP or later with Java installed, Mac OS X 10.8 or later with Java installed, Ubuntu Linux 12.04 LTS, or later with Java installed. Minimal RAM is based on the size of processed images, in this case, there are multiple images opened at once.

Use cases

A usability of introduced tools is presented in next sections. A use case for comparison for a CT measured with different slicing setup is presented. Results for a set of 5 CTs evaluated by different users is discussed. Since we were restricted by hardware, two versions of tool were created. One that is working with 8-bit version of images and needs less RAM, and one that works with 16-bit signed images and can load HU units.

Slice thickness variation

The international standard for saving DICOM files defines 3 mm slicing of CT data as the default way. However resaving data as TIFF (losing voxel information) or using different slice thicknesses (like 0.6 mm slicing) may result in a different result. In theory, 0.6 slicing would provide 5 times more detailed sampling in the Z-axis, however, in practice it is different.

The same CT dataset exported with 0.6 and 3 mm slices (XZ view for comparison is in Figure 3) was analyzed with our tool with a lung threshold of 0-155 and a pneumonia threshold of 47-115. The results can be found in Table 1. The error from a comparison of 3 mm and 0.6 mm slicing is estimated at 0.58 %. The used CT is available in the attached published dataset as CT1_1 (0.6 mm slicing) and CT1_2 (3 mm slicing).

Figure 3. XZ view comparison of 3 mm and 0.6 mm CT.

Table 1. Comparison of results from 0.6 mm and 3 mm 8-bit dataset.

Slicing	Lungs slices	Lungs threshold	Pneumoina threshold	Percentage	Scoring
0.6 mm	60-505	0-155	47-115	31.21	3
3 mm	12-101	0-155	47-115	31.79	3

User inter and intra variability

The biggest challenge in using this tool is an individual perception of images, as each person may see image data fundamentally the same - despite different appearances. Based on this a user can add the biggest bias even though the underlying data analysis is done correctly. The Table 2 contains a comparison of analysis results on 5 different CT datasets provided by the Faculty hospital of Královské Vinohrady. All of the CTs are analysed by users with various experience, the first CT exported with different slicing (also used in Table 1) is analysed by a radiologist (an expert user). The score aims to divide the percentage into groups based on previous research done,⁹ and should be the deciding factor of future care for patients.

Table 2. Independent analysis results.

Dataset	Slicing	Radiologist	Rad. score	User 1	User 1 score	User 2	User 2 score	User 3	User 3 score
CT1_1	0.6 mm	31%	3	50%	3	30%	3	30%	3
CT1_2	3.0 mm	32%	3	55%	4	33%	3	45%	3
CT2	3.0 mm	-	-	10%	2	5%	1	7%	1
CT3	0.6 mm	-	-	41%	3	24%	2	42%	4
CT4	3.0 mm	-	-	64%	4	64%	4	64%	4
CT5	3.0 mm	-	-	2%	1	3%	1	4%	1

Discussion

The ImageJ/FIJI tool can import various DICOM or TIFF files. Users should be always aware of whenever the saved data are using signed or unsigned bit depth, as unsigned data will shift pixel brightness. The same will happen when exporting data in different bit depth or with a specific CT view. The slicing of the CT dataset also matters, however, the analysis in Table 1 showed that it won’t significantly affect neither the percentage or the score (other CT machines might have different settings). A small case study for user inter and intra variability was made (Table 2) to evaluate the usability of the proposed tool. Some expected variability in results occur, interesting is inter variability in evaluating CT1 which is 3-5%. The intra variability is more extensive, up to 20%, and points out the fact that users should have at least some training in how to recognize pneumonia in CT images.

Conclusions

The tool was developed on demand from the Department of Radiology from the Faculty hospital Královské Vinohrady, as it was difficult for them to estimate the percentage and score of pneumonia in the lungs just by visually inspecting CT scans. Available hardware equipment and local account restrictions had to be taken into account for development tool selection. The ImageJ plugin is a compromise in accuracy and requirements. It logs all the user inputs for reproducibility and saves the results of all the steps as TIFF stacks. These masks and images can be used for visual inspection or possibly in the future for more advanced machine learning tools.

This software tool is the first step of a longer journey to create a tool that would be both easy to use for radiologists to diagnose COVID-19 based on CTs and include an advanced image analysis tool for percentage estimation of pneumonia in lungs. The use of open software promises ease of future development, however, it might be beneficial to move from ImageJ to 3D Slicer¹¹ or Napari¹² as they offer better tools for 3D visualization and integration of machine learning tools, which we aim to develop and integrate in our future works.

Limitations

The biggest limitation of this approach is human error, and inter and intra variation of manual selection. The percentage estimation might also be affected by other body cavities filled with air. There might also be a variance in results based on slice thickness, in worst case scenario 20%, but our experiment shows that there is only about 0.58% difference in result between 0.6 and 3mm CT slice thickness. The scoring should also be improved so it is not dependent only on one value (volume percentage), but normalized SHU distribution in the pneumonia area should be also considered.

Data availability

Underlying data

Zenodo: CT scans of COVID-19 patients, https://doi.org/10.5281/zenodo.5805939.¹³

This project contains the following underlying data:

• CT1_1
- – CT1_1_TIFF_06_MM (Single stack 8-bit TIFF data)
• CT1_2
- – CT1_2_TIFF_3_MM (Single stack 8-bit TIFF data)
• CT2
- – CT2_DICOM
- – CT2_TIFF (Single stack 8-bit TIFF data)
• CT3
- – CT3_DICOM
- – CT3_TIFF (Single stack 8-bit TIFF data)
• CT4
- – CT4_DICOM
- – CT4_TIFF (Single stack 8-bit TIFF data)
• CT5
- – CT5_DICOM
- – CT5_TIFF (Single stack 8-bit TIFF data)
• overview.csv (Detailed overview of each CT set.

Data are available under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0).

Software availability

Zenodo: ImageJ tool for percentage estimation of pneumonia in lungs, https://doi.org/10.5281/zenodo.5805989.¹⁴

This project contains the following underlying data:

• SEQUENCE_Est_Percentage_CT_16bit_V03_clean.ijm (16bit version)
• SEQUENCE_Est_Percentage_CT_u8bit_V03_clean.ijm (8bit version)
• results.csv (More detailed results of the dataset analysis)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Special acknowledgment goes to the Department of Radiology of Faculty hospital Královské Vinohrady, who provided the data, for medical support.

References

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3. Grajciarová L, Mareš J, Dvǒrák P, et al.: Software for diagnosis of the gpa disease using ct image analysis. International Conference on Applied Electronics. 2013. Reference Source
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5. Ebersberger U, Marcus RP, Schoepf UJ, et al.: Dynamic ct myocardial perfusion imaging: Performance of 3d semi-automated evaluation software. Eur. Radiol. 2014; 24(1): 191–199. Cited By: 34. PubMed Abstract | Publisher Full Text Reference Source
6. Behnaz AS, Snider J, Chibuzor E, et al.: Quantitative ct for volumetric analysis of medical images: Initial results for liver tumors. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2010; volume 7623. Cited By:8. Reference Source
7. Pfluger T, La Fougère C, Stauss J, et al.: Combined scanners (pet/ct, spect/ct) versus multimodality imaging with separated systems. Radiologe. 2004; 44(11): 1105–1112. Cited By:2. PubMed Abstract | Publisher Full Text Reference Source
8. Juergens KU, Grude M, Maintz D, et al.: Multi-detector row ct of left ventricular function with dedicated analysis software versus mr imaging: Initial experience. Radiology. 2004; 230(2): 403–410. Cited By:234. PubMed Abstract | Publisher Full Text Reference Source
9. Francone M, Iafrate F, Masci GM, et al.: Chest CT score in COVID19 patients: correlation with disease severity and short-term prognosis.July 2020; 30(12): 6808–6817. Publisher Full Text
10. Cromey DW: Avoiding twisted pixels: Ethical guidelines for the appropriate use and manipulation of scientific digital images.2010; 16(4): 639–667 June. Publisher Full Text
11. Kikinis R, Pieper SD, Vosburgh KG:3d slicer: A platform for subject-specific image analysis, visualization, and clinical support. In Intraoperative Imaging and Image-Guided Therapy. New York: Springer; November 2013; pages 277–289. Publisher Full Text
12. Sofroniew N, Lambert T, Evans K, et al.: napari/napari: 0.4.12rc2.2021. Reference Source
13. Schätz M, Rubešová O, Girsa D, et al.: CT scans of COVID-19 patients (Version V0) [Data set]. Zenodo. 2022. Publisher Full Text
14. Schätz M, Rubešová O, Mareš J, et al.: ImageJ tool for percentage estimation of pneumonia in lungs (Version V0). Zenodo. 2022. Publisher Full Text

Comments on this article Comments (0)

Version 3

VERSION 3 PUBLISHED 17 Mar 2022

Author details Author details

Martin Schätz
Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Olga Rubešová
Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Jan Mareš
Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

David Girsa
Roles: Methodology, Resources

Alan Spark
Roles: Validation

Competing interests

No competing interests were disclosed.

Grant information

The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.

Article Versions (3)

version 3

Revised

Published: 25 Jul 2025, 11:326

https://doi.org/10.12688/f1000research.109020.3

version 2

Revised

Published: 03 Jul 2023, 11:326

https://doi.org/10.12688/f1000research.109020.2

version 1

Published: 17 Mar 2022, 11:326

https://doi.org/10.12688/f1000research.109020.1

© 2022 Schätz M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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how to cite this article

Schätz M, Rubešová O, Mareš J et al. Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2022, 11:326 (https://doi.org/10.12688/f1000research.109020.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Open Peer Review

Version 1

VERSION 1

PUBLISHED 17 Mar 2022

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Reviewer Report 25 Oct 2022

Hamid A. Jalab, hamidjalab@um.edu.my Hamid A. Jalab Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia

Not Approved

https://doi.org/10.5256/f1000research.120473.r153263

The study describes a novel image tool developed for CT assessment of COVID-19 patients. The tool was created to assist in estimating the percentage of lungs infected by the virus.

The manuscript is interesting and addresses an important issue. Major comments:

1. The Introduction should provide a strong argument for why the software tool is important.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.

4. What are the benefits of the proposed approach above other current the new software tool?

Is the rationale for developing the new software tool clearly explained?

No
Is the description of the software tool technically sound?

No
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

No
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: image processing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Author Response 03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

03 Jul 2023

Author Response
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental ... Continue reading
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process.

Our replies to your comments:
1. The Introduction should provide a strong argument for why the software tool is important.
Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.
The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.
Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability.

4. What are the benefits of the proposed approach above other current the new software tool?
The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research.

The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested.

Is the rationale for developing the new software tool clearly explained?

We added a comparison to other freely available open source software tool as requested.

Is the description of the software tool technically sound?

We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool.

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation.
If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool.

In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process.

Our replies to your comments:
1. The Introduction should provide a strong argument for why the software tool is important.
Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.
The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.
Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability.

4. What are the benefits of the proposed approach above other current the new software tool?
The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research.

The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested.

Is the rationale for developing the new software tool clearly explained?

We added a comparison to other freely available open source software tool as requested.

Is the description of the software tool technically sound?

We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool.

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation.
If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool.

In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

03 Jul 2023

Author Response
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental ... Continue reading
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process.

Our replies to your comments:
1. The Introduction should provide a strong argument for why the software tool is important.
Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.
The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.
Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability.

4. What are the benefits of the proposed approach above other current the new software tool?
The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research.

The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested.

Is the rationale for developing the new software tool clearly explained?

We added a comparison to other freely available open source software tool as requested.

Is the description of the software tool technically sound?

We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool.

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation.
If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool.

In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process.

Our replies to your comments:
1. The Introduction should provide a strong argument for why the software tool is important.
Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.
The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.
Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability.

4. What are the benefits of the proposed approach above other current the new software tool?
The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research.

The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested.

Is the rationale for developing the new software tool clearly explained?

We added a comparison to other freely available open source software tool as requested.

Is the description of the software tool technically sound?

We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool.

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation.
If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool.

In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 10 Aug 2022

Tamas Dolinay, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.120473.r146294

Dr. Schätz and colleagues describe a new image analysis modality to quickly review and score lung CT scan images. The image analysis was applied to patients with COVID-19 pneumonia to aid with diagnostic accuracy. It is increasingly recognized that viral pneumonias, including COVID-19-associated pneumonia, may have a specific radiographic pattern. Automated modalities, including the one described by the authors can help with the rapid identification viral pneumonias and with measurement of the extent of the disease.

The manuscript is interesting and covers an important topic, but its current format suffers from a few weaknesses.

Major comments:

The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonias based on the imaging.
Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

Minor comments: Please write out the abbreviation when used for the first time to help the reader, who is not necessarily familiar with the specific language used.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Pulmonology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

03 Jul 2023

Author Response

Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped ... Continue reading Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts.

Our replies to your major comments:

1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging.

We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment.

The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article.

2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that.

In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts.

Our replies to your major comments:

1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging.

We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment.

The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article.

2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that.

In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

03 Jul 2023

Author Response

Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped ... Continue reading Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts.

Our replies to your major comments:

1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging.

We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment.

The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article.

2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that.

In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts.

Our replies to your major comments:

1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging.

We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment.

The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article.

2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that.

In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz
Competing Interests: No competing interests were disclosed. Close
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Version 3

VERSION 3 PUBLISHED 17 Mar 2022

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	1	2	3
Version 3 (revision) 25 Jul 25			read
Version 2 (revision) 03 Jul 23	read		read
Version 1 17 Mar 22	read	read

Tamas Dolinay, University of California, Los Angeles, Los Angeles, USA
Hamid A. Jalab, Universiti Malaya, Kuala Lumpur, Malaysia
Alessandro Santini, Humanitas University, Milan, Italy

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2 Views

19 Aug 2025 | for Version 3

Alessandro Santini, Department of Biomedical Sciences, Humanitas University, Milan, Italy

2 Views Cite this report Responses(0)

Approved

I thank the Authors for their review and update of the manuscript, which I feel is now suitable for indexing.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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16 Views

01 Nov 2023 | for Version 2

Alessandro Santini, Department of Biomedical Sciences, Humanitas University, Milan, Italy

16 Views Cite this report Responses(2)

Approved With Reservations

The Authors present a new tool for quantitative analysis of CT scans of COVID-19 patients. The novelty of this tool, compared to already available softwares such as 3D Slicer, is its availability (the code is open-source) and the alleged ease of use even by non-experienced users.

However, when presenting results of inter- and intra-user variability of results, the Authors show not very promising results. They also acknowledge this problem in the discussion: "The biggest challenge in using this tool is an individual perception of images (...) Based on this a user can add the biggest bias even though the underlying data analysis is done correctly". While I agree with the Authors, I do not see how this problem is overcome by their tool. The Authors later in the manuscript state that "this challenge can be addressed by using standardized procedures and guidelines, multiple raters for segmentation, and computer-aided methods". However, it is not clear from this paper that this challenge has efficiently being addressed/solved.
This is not a secondary issue for a tool which is designed to be "user-friendly" and easy to use even by a non-experienced user.

I suggest the Authors to expand their inter- and intra-user variability analysis. While inter-user variability is presented, I did not find any data on intra-user variability (same user performing the analysis on a single CT more than once). You can use as a guide the following ref: Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324.

Furthermore, as the tool is supposed to give the same results as other, already available, softwares, the Authors should present a comparison between the results obtained with their tool and the results obtained with other(s) software(s) on their CT scans.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

References

1. Popović ZB, Thomas JD: Assessing observer variability: a user's guide.Cardiovasc Diagn Ther. 2017; 7 (3): 317-324 PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine

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Author Response

30 Nov 2023

Martin Schätz, Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic

Dear Dr. Alessandro Santini,

we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments.

We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer (https://imaris.oxinst.com/imaris-viewer), ZEISS ZEN lite (https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment.

We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations.

Once again, we sincerely appreciate your invaluable feedback.

Best regards,
Martin Schätz

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Competing Interests

No competing interests were disclosed.

Author Response

09 Aug 2025

Martin Schätz, Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic

Dear reviewer,

thank you very much for insightfull feedback and recomendations to improve our analysis of our tool.
Addressing User Bias and Tool's Focus
We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice.

Intra- and Inter-User Variability Analysis
We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper.

Comparison with Existing Software
Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently, if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository (https://github.com/Slicer/SlicerLungCTAnalyzer). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow.
We agree the comparison is a crucial point for future versions and further analysis of our tool.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

3 Views

18 Jul 2023 | for Version 2

Tamas Dolinay, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

3 Views Cite this report Responses(0)

Approved

The authors have sufficiently revised the manuscript. I believe the revised paper is an important addition to the growing field of CT imaging in COVID-19 lung disease.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Pulmonology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

24 Views

25 Oct 2022 | for Version 1

Hamid A. Jalab, hamidjalab@um.edu.my Hamid A. Jalab Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia

24 Views Cite this report Responses(1)

Not Approved

Is the rationale for developing the new software tool clearly explained?

No
Is the description of the software tool technically sound?

No
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

No
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

image processing

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Author Response

03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

Dear Dr. Hamid A. Jalab,

I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process.

Our replies to your comments:
1. The Introduction should provide a strong argument for why the software tool is important.
Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository.

2. It is of importance to have sufficient results to justify the novelty of the proposed software tool.
The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them.

3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section.
Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability.

4. What are the benefits of the proposed approach above other current the new software tool?
The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research.

The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested.

Is the rationale for developing the new software tool clearly explained?

We added a comparison to other freely available open source software tool as requested.

Is the description of the software tool technically sound?

We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool.

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation.
If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool.

In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

29 Views

10 Aug 2022 | for Version 1

Tamas Dolinay, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

29 Views Cite this report Responses(1)

Approved With Reservations

The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonias based on the imaging.
Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

Minor comments: Please write out the abbreviation when used for the first time to help the reader, who is not necessarily familiar with the specific language used.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Pulmonology

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Author Response

03 Jul 2023

Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic

Dear Dr. Tamas Dolinay,

I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts.

Our replies to your major comments:

1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging.

We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment.

The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article.

2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world?

The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that.

In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository.

Thank you again for your valuable feedback.
Martin Schätz

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Estimation of Covid-19 lungs damage based on computer tomography images analysis

Abstract

Keywords

Introduction

Methods

Image format

Implementation

Figure 1. Result of analysis as RGB stack, where Red channel contains CT data, Green channel lung mask and Blue channel pneumonia mask.

Operation

Figure 2. ImageJ Tool, loading data options.

Use cases

Slice thickness variation

Figure 3. XZ view comparison of 3 mm and 0.6 mm CT.

Table 1. Comparison of results from 0.6 mm and 3 mm 8-bit dataset.

User inter and intra variability

Table 2. Independent analysis results.

Discussion

Conclusions

Limitations

Data availability

Underlying data

Software availability

Acknowledgements

References

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