PONE-D-21-31343SinGAN-Seg: Synthetic training data generation for medical image segmentationPLOS ONE

Dear Dr. Thambawita,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The paper needs significant improvement. It should position the proposed method with respect to the state of the art in GAN-based synthetic data generation - detail the contribution, show the advantages, put the results in comparison. It should also explain the improvement of results due to style transfer. More metrics should be employed to evaluate the generated images.

Please submit your revised manuscript by Jan 24 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Ruxandra Stoean

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1) First of all, the figures need to be significantly improved. Right now they are of very poor quality and very blurred. It is hard for the reviewers to make sense of the figures as they are. Use previously published PLOS ONE papers as a reference.

2) There are many spelling mistakes. Please run the manuscript through a spell check.

3) The proposed approach uses state-of-the-art generation approach SinGAN to generate synthetic images along with the corresponding ground-truth followed by a style transfer. This is not a significant novelty other than the part of generating the corresponding ground-truth.

4) The last 2 contributions of the paper are not new:

a) "We show that synthetic images and corresponding mask images can improve the segmentation performance when the size of a training dataset is limited."

b) "We show that synthetic data can achieve a very close performance to the real data when the real segmentation datasets are large enough."

Recent works such as [1 - 3] have already shown this with medical images. The references are missing.

[1] Theagarajan, R. and Bhanu, B., 2019. DeephESC 2.0: Deep generative multi adversarial networks for improving the classification of hESC. PloS one, 14(3), p.e0212849.

[2] Witmer, A. and Bhanu, B., 2018, October. HESCNET: A Synthetically Pre-Trained Convolutional Neural Network for Human Embryonic Stem Cell Colony Classification. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 2441-2445). IEEE.

[3] Jonnalagedda, P., Weinberg, B., Allen, J., Min, T.L., Bhanu, S. and Bhanu, B., 2021, January. SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 4941-4948). IEEE.

4) quantitative evaluation of the generated synthetic images are required. The authors need to provide scores such as the FID and SIFID scores as used in the SinGAN paper.

4) The results provided in Tables 1 and 2 are not sufficient. Since training data is very important, how are the qualities of the generated images verified in Tables 1 and 2? One suggestion would be to compute the quality metric (FID, SIFID, etc.) of the generated images and take only the top X% of the synthetic images for training

Reviewer #2: The authors have developed a method for generating synthetic data for medical image segmentation based on sinGAN and style transfer. It is an interesting study to deal with privacy and small dataset of medical images. However, there is no comparison with existing methods, and the superiority of the proposed method is not clear. The reviewer suggests the authors to revise the manuscript according to the following comments.

SinGAN-Seg as data sharing technique

1. Please add a comparison of the proposed method with other data augmentation methods (e.g., conventional GAN). What is the advantage of the proposed method over the conventional GAN based synthetic data generation?

SinGAN-Seg with small datasets

2. As mentioned above, please highlight the superiority of the proposed method compared to other common data augmentation methods for small datasets analysis, too.

3. Did you apply style transfer in the results of Table 2 and Fig.11? Please clarify how style transfer improves the results.

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Reviewer #1: No

Reviewer #2: No

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PONE-D-21-31343R1SinGAN-Seg: Synthetic training data generation for medical image segmentationPLOS ONE

Dear Dr. Thambawita,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Improvement is still needed regarding experimental comparison and formatting.

Please submit your revised manuscript by Mar 26 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ruxandra Stoean

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1) The authors mentioned that they have uploaded better quality images, but I still do not see any improvement in the quality of the images.

2) There are still some minor spelling mistakes, please check one more time. Line 93 date -> data.

3) Table 1 in the revised manuscript is very poorly formatted. I cannot see anything, except the first column. Please check the manuscript before uploading for review!

4) The authors mention and discuss the quality of the generated images using SinGan-seg throughout the paper but, there is no comparison with other GAN related works. Hence it is not possible to validate if the proposed approach is significantly better in terms of quality, other than the fact that it requires only 1 training image.

4.1) Although SinGan-seg takes only 1 input to train compared to conventional GANs that require more images, the authors need to generate results using at least 2 - 3 other approaches that use conventional GANs (even if it requires more training images compared to SinGan-seg). We need to see if there is significant difference in quality of generated images between the different approaches. This comparison will greatly benefit the readers to understand if the difference in no. of training images Vs. quality of generated images is significantly different.

4.2) Similarly compare the SIFID scores of SinGan-seg with these other approaches.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

SinGAN-Seg: Synthetic training data generation for medical image segmentation

PONE-D-21-31343R2

Dear Dr. Thambawita,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Ruxandra Stoean

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper has been formatted well. The authors have addressed all my comments and necessary statistical experiments have been performed.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No