-->PONE-D-25-68120-->-->A public generalizable AI tool for automated segmentation of coronal brain tissue slabs for 3D neuropathology-->-->PLOS One

Dear Dr. Williams Ramirez,

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“This research was primarily funded by the National Institute of Aging, https://www.nia.nih.gov/, (1R01AG070988 (JEI)). Additional support was provided by the National Institute of Mental Health, https://www.nimh.nih.gov/, (1RF1MH123195 (JEI, BF), 1UM1MH130981), the National Institute of Aging, https://www.nia.nih.gov/, (1RF1AG080371 (JEI)), the National Institute of Biomedical Imaging and Bioengineering, https://www.nibib.nih.gov/, (1R01EB031114), and the National Institute of Neurological Disorders and Stroke, https://www.ninds.nih.gov/, (1R21NS138995). The UW BioRepository and Integrated Neuropathology (BRaIN) laboratory is supported by the National Institutes of Health (NIH), by the National Institute of Mental Health, https://www.nimh.nih.gov/, through the the BRAIN Initiative Cell Atlas Network (UM1MH134812 (CDK)); also by the National Institute of Aging, https://www.nia.nih.gov/, through the UW Alzheimer’s Disease Research Center (P30 AG066509 (CDK)), the Kaiser Permanente Washington Adult Changes in Thought (ACT) study (U19 AG066567 (CDK)), the Seattle Alzheimer’s Disease Brain Cell Atlas (U19AG060909 (CDK)), also multiple collaborative projects supporting imaging, digital pathology, neuropathology, and related research tools (U24AG072458 (CDK); through the National Institute of Neurological Disorders and Stroke, https://www.ninds.nih.gov/, U24NS133949; U24NS133945; U24NS135651; U01NS137500; and U01NS137484 (CDK)), the US Department of Defense, https://www.war.gov/, (DoD W81XWH-21-S- TBIPH2), the Allen Institute for Brain Science, and the Nancy and Buster Alvord Endowment (to CDK). Support for this research was also provided in part by the BRAIN Initiative Cell Atlas Network (BICAN) grants, through the National Institute of Mental Health, https://www.nimh.nih.gov/, U01MH117023 (BF), UM1MH134812 (CDK) and UM1MH130981, the Brain Initiative Brain Connects consortium, through the National Institute of Neurological Disorders and Stroke, https://www.ninds.nih.gov/, (U01NS132181 (BF), 1UM1NS132358-01), the National Institute for Biomedical Imaging and Bioengineering, https://www.nibib.nih.gov/, (1R01EB023281 (BF), R21EB018907 (BF), R01EB019956 (BF), P41EB030006 (BF)), the National Institute on Aging, https://www.nia.nih.gov/, (R21AG082082 (BF), 1R01AG064027 (BF), R01AG016495, 1R01AG070988 (JEI)), the National Institute of Mental Health, https://www.nimh.nih.gov/, (UM1MH130981, R01 MH123195 (BF, JEI), R01 MH121885, 1RF1MH123195 (BF, JEI)), the National Institute for Neurological Disorders and Stroke, https://www.ninds.nih.gov/, (1U24NS135561-01 (BF), R01NS070963 (BF), 2R01NS083534 (BF), R01NS105820 (BF), R25NS125599 (BF)), and was made possible by the resources provided by Shared Instrumentation Grants, through National Center for Research Resources, http://www.ncrr.nih.gov/, 1S10RR023401 (BF), 1S10RR019307 (BF), and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research through National Institute of Mental Health, https://www.nimh.nih.gov/, (5U01-MH093765), part of the multi institutional Human Connectome Project. Much of the computation resources required for this research was performed on computational hardware generously provided by the Massachusetts Life Sciences Center (https://www.masslifesciences.com). The authors would like to thank the research participants and their families without whom this work would be impossible. “

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“Dr Hyman owns stock in Novartis; he serves on the SAB of Dewpoint and has an option for stock. He serves on a scientific advisory board or is a consultant for AbbVie, Alexion, Ambagon, Aprinoia Therapeutics, Arbor Bio, Arvinas, Avrobio, AstraZenica, Biogen, Bioinsights, BMS, Cell Signaling, Cure Alz Fund, CurieBio, Dewpoint, Etiome, Latus, Merck, Novartis, Paragon, Pfizer, Sanofi, Sofinnova, SV Health, Takeda, TD Cowen, Vigil, Violet, Voyager, WaveBreak. His laboratory is supported by research grants from the National Institutes of Health, Cure Alzheimer’s Fund, Tau Consortium, and the JPB Foundation – and sponsored research agreement from Abbvie and Sanofi. He has a collaborative project with Biogen and Neurimmune. Dr Fischl is an advisor to DeepHealth, a company whose medical pursuits focus on medical imaging and measurement technologies. BF's interests were reviewed and are managed by Massachusetts General Hospital and Mass. General Brigham in accordance with their conflict of interest policies. “

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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: Yes

Reviewer #2: Yes

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

Reviewer #1: I Don't Know

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 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: Yes

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: Below is a set of suggestions and recommendations for the improvement of your manuscript, "A public generalizable AI tool for automated segmentation of coronal brain tissue slabs for 3D neuropathology". This work addresses a critical bottleneck in quantitative neuropathology by automating the time-consuming process of manual tissue segmentation. Your results, achieving a median Dice score over 0.98 and performance approaching inter-/intra-rater levels, are highly impressive.

To further strengthen the manuscript for publication in PLOS One, please consider the following points:

1. Elaborate on Generalization and Site Diversity

While the model demonstrates strong robustness on out-of-distribution data, you correctly identify that data diversity is a limitation, as training and evaluation involved only three sites.

Suggestion: It would be beneficial to expand the discussion on how the tool might perform in more "extreme" out-of-distribution scenarios, such as varying lighting conditions, non-standard backgrounds, or different camera angles. Additionally, explicitly recommending future external validation by independent institutions with different photographic protocols would provide a clearer roadmap for the tool's adoption.

2. Qualitative Analysis of Segmentation Outliers

The study notes that 6% of the out-of-distribution UW-fixed images exhibited errors (mean surface distance) greater than 2 mm.

Suggestion: I recommend adding a brief qualitative analysis or a supplementary figure illustrating these outlier cases. Identifying if these errors were driven by tissue artifacts, excessive glare, or poor contrast between the cortex and the background would help future users understand the specific conditions where manual refinement is most likely needed.

3. Towards Full Automation of Calibration

Currently, for the MADRC and UW-fixed datasets, pixel size calibration still requires manual clicks on reference rulers. In contrast, the UW-fresh dataset utilizes an automated detection of fiducial markers.

Suggestion: The authors should discuss the feasibility of automating ruler detection for the other protocols. This would move the pipeline toward being fully autonomous, further reducing the "laborious manual intervention" mentioned in your abstract.

4. Refining the Handling of Non-Target Slabs

You mention that the presence of non-target slabs in the background can lead to irrelevant false positives which are currently factored out of accuracy metrics.

Suggestion: To improve the "out-of-the-box" user experience of the public tool, please clarify if the released version includes an automated post-processing step (such as a "largest connected component" filter) to remove these artifacts automatically, or if users must still perform this masking manually.

5. Data Availability and Repository Maintenance

As a requirement for PLOS One, all data underlying the findings must be fully available.

Suggestion: Ensure that the provided link to the FreeSurfer software suite and the specific tool repository is active and that any necessary documentation for external users to run the model on their own data is clearly outlined.

Conclusion: This is a technically sound and well-written manuscript that provides a valuable open-source contribution to the field. Addressing the points above will enhance the transparency regarding the tool's limitations and its practical utility for the broader neuropathology community.

Reviewer #2: 1- Please clarify the research gap further in the abstract.

2- Rewrite the contributions as clear and concise bullet points.

3- Add a table summarizing related works, including the year of publication, the dataset used, the advantages and disadvantages of each research paper, and the nature of the technology used.

4- Add the conclusions reached in the manuscript along with the proposed future works.

5- Add at least three references published in 2025

6-Add a flowchart summarizing the proposed method.

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Reviewer #1: No

Reviewer #2: No

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A public generalizable AI tool for automated segmentation of coronal brain tissue slabs for 3D neuropathology

PONE-D-25-68120R1

Dear Dr. Williams Ramirez,

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,

Federico Giove, PhD

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

Reviewer #2: (No Response)

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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: Yes

Reviewer #2: (No Response)

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

Reviewer #1: Yes

Reviewer #2: (No Response)

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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: (No Response)

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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: (No Response)

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-->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: (No Response)

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

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