Dear Dr. Chou,

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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: No

Reviewer #2: N/A

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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: No

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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: This paper introduces a model fusion strategy to enhance brain tissue segmentation performance using U-Nets. The proposed technique is straightforward to implement and showed improved performance compared to standalone U-Nets. The paper is written in an easy-to-understand manner. Here are some comments for consideration:

Major comments:

• It seems that all the results from each segmentation model were generated through one trial. To improve the validity of the observations, please train the models multiple times and report the overall segmentation results (e.g., mean and standard deviation of evaluation metrics).

• In the Discussion section, you mention “the cumulative effect on performance may not be significant.” Please include the segmentation results of the fusion of U-Net and UNet3++ to substantiate this claim.

• It is suggested that the authors can provide theoretical explanations for the performance differences among the three U-Nets used in the study based on their architectural differences. This can offer valuable insights to readers regarding the preferable U-Net architecture for the brain tissue segmentation task.

• The computational cost of different segmentation strategies should be discussed in the Discussion section.

Minor comments:

• In the Materials and Methods -- Training section, please use the full name of “BCELoss” (Binary Cross Entropy loss).

• In the Materials and Methods – U-Net series for BTS section, please avoid using “left branch” and “right branch” to describe the encoder and decoder of U-Nets. Such ambiguous descriptions can be misleading.

• In the descriptions of Table 5-7, please specify the results are from the COCO cat segmentation task.

• In Figure 1 caption “the over-segmented results by some brain tissue segmentation algorithms”: please specify the names of the brain tissue segmentation algorithms used.

Reviewer #2: This work describes a fusion framework that combines the prediction of multiple UNet models to improve the overall classification accuracy of the model. The following minor comments and questions should be addressed before the paper is ready for publication:

1. This work uses simple intersection as the fusion operation, while more complex, nonlinear forms of fusion exist, such as an element-wise multiplication (of the latent space, not the final prediction). See https://doi.org/10.1016/j.cma.2023.116277 and https://doi.org/10.1007/s10915-022-01881-0 for examples. The authors should discuss whether those nonlinear forms of data fusion have a potential use in the current case.

2. For classification models, logits are typically generated and then passed through a final activation layer to generate the binary classification output. In this case, the authors chose to perform fusion on the activated output, what about the possibility of performing fusion on the logits, then apply a single activation to the fused logits? The authors can maybe discuss a bit along this line.

3. It seems like the authors manually varied some hyper-parameters of the UNet models to investigate their effectiveness. automatic hyperparameter optimization models have been widely explored and applied, such as https://doi.org/10.1002/widm.1484, https://doi.org/10.1007/s00707-023-03691-3. Relevant works should be discussed in the literature review.

4. The authors mentioned "However, having more fusion strategies means training more models, which might not offer significant benefits compared to training a single, more complex model.". This is an interesting point that was not supported by data in the current manuscript. The authors should list the number of trainable parameters and training times for each model used in this work, and compared the fusion results (both in terms of training time and accuracy) with a single UNet model with comparable number of trainable parameters (as the two fused models combined).

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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Refining CT image analysis: Exploring adaptive fusion in U-Nets for enhanced brain tissue segmentation

PONE-D-24-17378R1

Dear Dr. Chou,

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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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Xiaohui Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: 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

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. 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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5. 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: The authors have addressed all the previous comments. I do not have additional comments. The manuscript is ready for the next stage.

Reviewer #2: The authors have sufficiently addressed the reviewer comments, and the manuscript can be considered for publication.

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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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