Dear Dr. Tank,

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.

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Diego Raimondo

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?

Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: In this study, the authors present a deep learning-based model for uterus segmentation in transvaginal ultrasound.

However, I believe the title of the manuscript should be reconsidered. Referring to the study as a demonstration of “feasibility” is questionable, given the substantial number of existing publications on ultrasound segmentation. The concept is no longer novel in itself.

There is also a conceptual inaccuracy in referring to both U-Net and nnU-Net as deep learning segmentation models. U-Net is a convolutional neural network (CNN) architecture, whereas nnU-Net is a self-configuring framework that uses U-Net as a backbone. This distinction is critical. Comparing the two is of limited value, as the superiority of nnU-Net as a comprehensive framework—including preprocessing, data augmentation, and parameter tuning—has been well established in the literature.

The introduction lacks coverage of key studies on deep learning-based segmentation in ultrasound beyond obstetrics. Important applications in breast, prostate, intraoperative brain tumor imaging, and thyroid ultrasound should be cited to better contextualize the work.

Furthermore, there is no clear description of the segmentation masks used—no details are provided regarding mask dimensions, range of values, or variability.

Lastly, and most importantly, we are currently facing a reproducibility crisis in the field of AI in medicine. It is imperative that studies of this nature be accompanied by a public repository including at least the trained model weights or checkpoints. This would enable other researchers to test the model on their own data and verify the findings.

Without addressing these issues, the manuscript falls short of the standards required for publication.

Reviewer #2: The aim of this study was to train and evaluate two deep learning (DL) segmentation models, U-Net and nnU-Net, to analyze the feasibility of DL-based uterus segmentation on transvaginal ultrasound (TVUS).

I have the following comments to the Authors:

• Please check for typos and tense mistakes throughout the text.

• Introduction

• Authors did not highlight enough the importance of the problem and why this study was necessary

• Authors should add in the introduction a brief revision about what is already known about the study subject, including other examples of DL applications in gynecologic pathologies [e.g. PMID: 38610993] and ultrasound diagnosis of adenomyosis [e.g. PMID: 38738458].

• Methods

• The study design is not clear, please state it explicitly at the beginning of materials and methods section.

• What was the setting in which the study was conducted?

• Authors should describe more in details the characteristics of the participants of the study used to create the image bank. In particular, how were selected the patients included in the study (inclusion and exclusion criteria) and how was selection bias avoided.

• Discussion

- Authors should include a brief qualitative resume of their results at the beginning of discussion

- Authors should include in the discussion a comparison of their study with similar studies already present in literature

- In order to make the article more praiseworthy, Authors should include a paragraph analyzing strengths and limitations of their study, for example discussing about the fact that the DL model was trained on a very specific population of uteruses (i.e. uteruses with adenomyosis).

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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

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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.

Automatic uterus segmentation in transvaginal ultrasound using U-Net and nnU-Net

PONE-D-25-08028R1

Dear Dr. Tank,

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,

Paolo Cazzaniga

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All reviewers' comments have been addressed by the authors, improving the overall quality of the manuscript, which can now be accepted for publication

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

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

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

Reviewer #1: No

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

Reviewer #1: Yes

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Reviewer #1: The authors have added clarifications and modifications to the manuscript, addressing my concerns and, in my opinion, improving the overall quality of the paper. Therefore, I recommend it for publication in its current form.

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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

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