PCOMPBIOL-D-25-01047

Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning

PLOS Computational Biology

Dear Dr. Zhou,

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Kind regards,

Jinshan Xu

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one of the reviews is uploaded as an attachment.

Reviewer #1: uploaded as attachment

Reviewer #2: This paper proposes a novel multi-scale segmentation pipeline that enables the segmentation of micro-scale functional units across entire organs, thereby facilitating the analysis of functional relationships and spatial distributions within the context of whole-organ anatomy. The proposed framework is innovative, particularly in its use of generated histograms of morphological and spatial properties to filter outliers. The authors conducted extensive experiments and have made both their dataset and code publicly available. This work contributes to the enhanced utilization of HiP-CT and demonstrates significant potential for medical applications. The reviewer encourages the authors to address the following concerns:

1. The necessity and effectiveness of fine-tuning and transfer learning are not clear. In terms of feature extraction, the feature space differs substantially across resolutions. The reviewer recommends that the authors provide additional validation regarding the effectiveness of fine-tuning a model trained on high-resolution data when applied to low-resolution data.

2. The overfitting behavior observed in UNETR lacks a thorough analysis. As shown in Figure 3, UNETR exhibits more pronounced overfitting compared to the other three methods. The reviewer suggests that the authors include an analysis of the possible causes of this phenomenon.

Reviewer #3: The authors describe a multiscale strategy for deep learning with high, and low resolution images, here HiP-CT scans of the kidney.

Their strategy is to mutiscale across scales in the following way: High-resolution predictions are propagated to lower resolutions, which creates training pseudo-labels for models specific to coarser scales. In this way, they are able to achieve segmentation not only at organ-level but also a glomerular levels.

This is a promising approach that can move the field forward.

My difficulty in reading the paper was they introduce the various components of their pipeline woven into the numerous sections in the paper. There is no clear outline of the proposed strategy and what networks they use in each stage. Further, the terminology "pseudo-labels" was very confusing as the authors never quite define what they mean.

Overall, the paper has a lot of valuable details to offer for the interested practitioner, but it is difficult to read this without first having some bearings as to what the authors wanted to achieve and how did they plan to go about it.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes: Jens J. G. Lohmann

Reviewer #2: Yes: Tao Chen

Reviewer #3: No

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Attachments

Attachment

Submitted filename: review_PLOS.docx

Dear Dr Zhou,

We are pleased to inform you that your manuscript 'Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Jinshan Xu

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Thanks to the authors for addressing my comments in such detail!

I have no further comments to add.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code 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 and code 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 or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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Reviewer #1: Yes: Jens J G Lohmann