Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks

Kayla L. Stanke; Ryan J. Larsen; Laurie Rund; Brian J. Leyshon; Allison Y. Louie; Andrew J. Steelman

doi:10.1371/journal.pone.0284951

Peer Review History

Original SubmissionDecember 2, 2021
2 Dec 2021 Author Response Attachments Attachment Submitted filename: response_to_reviewer.docx https://doi.org/10.1371/journal.pone.0284951.r001
16 Jul 2022 Decision Letter - Zhishun Wang, Editor PONE-D-21-37916Automated identification of piglet brain tissue from MRI images using Region-Based Convolutional Neural NetworksPLOS ONE Dear Dr. Larsen, 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. Please submit your revised manuscript by Aug 20 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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The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf. 2. Thank you for stating the following in the Acknowledgments Section of your manuscript: “The study was funded by Abbott Nutrition, www.abbott.com, grant number 00490326 to A.S. The funder aided with study design, data analysis, decision to publish, and preparation of the manuscript. We thank the staff members of the Biomedical Imaging Center at the Beckman Institute for Advanced Science and Technology for imaging support. “ We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: “The study was funded by Abbott Nutrition, www.abbott.com, grant number 00490326 to A.S. The funder aided with study design, data analysis, decision to publish, and preparation of the manuscript.” Please include your amended statements within your cover letter; we will change the online submission form on your behalf. [Note: HTML markup is below. Please do not edit.] 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: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: No ****** 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: No ****** 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: No ****** 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: Overall, the segmentation results appear to be very good, and the cross-fold validation study is rigorous. Splitting the folds over pigs (subject-wise split) avoids data contamination issues between training and testing, which is good. However, I do not like how the outlier case of one pig was manually-corrected and then the segmentation values were used to report final Dice scores. Manual-correction is fine, but I feel that this is a biased presentation of results. Instead, I encourage you to report both the Dice overlap scores using the original data (that includes the failure case) as well as the results after manual correction for full transparency of results. Imaging artefacts happen all the time, and I think presenting these full results would be more useful for readers. What form of image intensity normalization was performed? Intensity normalization for MR images is often a critical component. It might not be problem here since all images were from the same scanner, but it would be problem if different scanners were used (or the same scanner was used after any upgrades). Intensity normalization could also help to control for intensity outliers (as seen in your failure case). Please elaborate on any normalization methods used. Largest connected component filtering was performed as a post-processing step. It would be helpful to report the Dice segmentation values prior to post-processing as well. This would give readers a sense of how well the original segmentation performed compared to how much the post-processing helped. The inclusion of Dice overlap as similarity metric is good and gives a measure of overall segmentation performance. But, typically, Dice by itself is not the only metric used for segmentation evaluation. Other metrics that are commonly used are Harsdorff Distance (HD) and Mean Surface Distance. In particular, HD is of interest because this gives a measure of worst-case segmentation performance. Worst case segmentation performance is often of interest to readers. Please consider including HD (or its less extreme variant the 95-th percentile of HD). Training and inference used 2D image slices. During inference, all 2D slices of a piglet were combined into a 3D volume. This has the potential to result in mask results that are not smooth at the boundary because each 2D slice is independent of its neighbors (this is where 2.5D or 3D processing would help). Please discuss the spatial consistency of results between neighboring slices within the 3D volume. Grammar/Typographical: Line 134: “all by the” -> “all but the” Reviewer #2: I have the following concerns about this manuscript: 1- This manuscript is written by a way different from usual known articles including sectioning and subsectioning, references ..etc. 2- There is no mathematical or statistical analysis at all. 3- The methodology used in training, validation, and testing the used model is unclear. 4- The author should compare their results with related published references performing the segmentation task using the same data set without restriction themselves to the piglet MRI images. ****** 6. 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: Yes: Ashraf A. M. Khalaf ******** [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. https://doi.org/10.1371/journal.pone.0284951.r002
Revision 1
12 Dec 2022 Author Response Reviewer #1: Overall, the segmentation results appear to be very good, and the cross-fold validation study is rigorous. Splitting the folds over pigs (subject-wise split) avoids data contamination issues between training and testing, which is good. However, I do not like how the outlier case of one pig was manually-corrected and then the segmentation values were used to report final Dice scores. Manual-correction is fine, but I feel that this is a biased presentation of results. Instead, I encourage you to report both the Dice overlap scores using the original data (that includes the failure case) as well as the results after manual correction for full transparency of results. Imaging artefacts happen all the time, and I think presenting these full results would be more useful for readers. Response: We thank the reviewer for their helpful review. We agree with the reviewer and we have revised the manuscript to report Dices scores before and after this manual correction. What form of image intensity normalization was performed? Intensity normalization for MR images is often a critical component. It might not be problem here since all images were from the same scanner, but it would be problem if different scanners were used (or the same scanner was used after any upgrades). Intensity normalization could also help to control for intensity outliers (as seen in your failure case). Please elaborate on any normalization methods used. Response: We agree with the reviewer on the importance of intensity. We have revised the manuscript to state that we did not perform normalization, and to state that might have prevented the segmentation error mentioned by the reviewer, and that it would likely be important for use with data from other scanning conditions. Largest connected component filtering was performed as a post-processing step. It would be helpful to report the Dice segmentation values prior to post-processing as well. This would give readers a sense of how well the original segmentation performed compared to how much the post-processing helped. Response: We have updated the manuscript to include this information, and to quantify the improvements in Dice coefficients due to largest connected component filtering The inclusion of Dice overlap as similarity metric is good and gives a measure of overall segmentation performance. But, typically, Dice by itself is not the only metric used for segmentation evaluation. Other metrics that are commonly used are Harsdorff Distance (HD) and Mean Surface Distance. In particular, HD is of interest because this gives a measure of worst-case segmentation performance. Worst case segmentation performance is often of interest to readers. Response: We have updated the manuscript to report Hausdorff Distance measures. Training and inference used 2D image slices. During inference, all 2D slices of a piglet were combined into a 3D volume. This has the potential to result in mask results that are not smooth at the boundary because each 2D slice is independent of its neighbors (this is where 2.5D or 3D processing would help). Please discuss the spatial consistency of results between neighboring slices within the 3D volume. Response: We have updated the manuscript with a discussion of the potential advantages of 3D processing for improvement of consistency between slices. Grammar/Typographical: Line 134: “all by the” -> “all but the” Response: Thank you for pointing this out; it has been corrected. Reviewer #2: I have the following concerns about this manuscript: 1- This manuscript is written by a way different from usual known articles including sectioning and subsectioning, references ..etc. Response: We thank the reviewer for their review and comments. We believe that the sectioning is consistent with the usual pattern, including Introduction, Methods, Results, and Discussion. The Methods section is sub-sectioned into Animals and Care Practices, MRI Acquisition, Manual Brain Extraction, Automated Brain Extraction, and Validation. 2- There is no mathematical or statistical analysis at all. Response: We have updated the manuscript to include Hausdorff Distance values, and some descriptive statistics of them. 3- The methodology used in training, validation, and testing the used model is unclear. Response: We tested the model using nested cross-validation. We do not have a separate validation set because we did not perform hyperparameter tuning. The method of nested cross-validation and the potential usefulness of hyperparameter tuning is described in the paper. 4- The author should compare their results with related published references performing the segmentation task using the same data set without restriction themselves to the piglet MRI images. Response: We agree that the inclusion of more data could strengthen the manuscript; however, we believe that the inclusion of non-piglet MRI images is beyond the scope of the manuscript. Attachments Attachment Submitted filename: reviewer_comments_responses.docx https://doi.org/10.1371/journal.pone.0284951.r003
26 Jan 2023 Decision Letter - Zhishun Wang, Editor PONE-D-21-37916R1Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural NetworksPLOS ONE Dear Dr. Larsen, 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. Please submit your revised manuscript by Mar 12 2023 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, Zhishun Wang, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] 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) ******** 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: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: No ****** 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 ****** 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 ****** 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 authors have addressed my comments. The main limitation of the paper’s evaluation is that the segmentation approach is not compared to alternative segmentation methods as a benchmark. For example, comparison to the 3D U-net approach in [12] using the data from this paper would be ideal. The method in [12] is another segmentation approach applied to very similar data, piglet brain MRI. The proposed approach to use Mask R-CNN would be rigorously justified with a direct head-to-head comparison with the 3D U-net using your dataset. Absent this numerical comparison, additional discussion comparing to the U-net approach to the proposed approach would be helpful in the Discussion section. The performance of [12] is briefly mentioned in the Discussion (line 223), but a more thorough comparison to the proposed method (both strengths and weaknesses) may be warranted since it is a such a similar application area. However, this discussion must note that a true comparison cannot be made because the two approaches used different datasets. Reviewer #2: It is the second time to review this manuscript, and almost all my concerns didn't addressed: 1- This manuscript is written by a way different from usual known articles including sectioning and subsectioning, references ..etc. Response: We thank the reviewer for their review and comments. We believe that the sectioning is consistent with the usual pattern, including Introduction, Methods, Results, and Discussion. The Methods section is sub-sectioned into Animals and Care Practices, MRI Acquisition, Manual Brain Extraction, Automated Brain Extraction, and Validation. Reviewer again: Where is the "Conclusion" section? Please, see one of the papers published y PloS One: Zhai Y, Davenport B, Schuetz K, Pappu HR (2022) An on-site adaptable test for rapid and sensitive detection of Potato mop-top virus, a soilborne virus of potato (Solanum tuberosum). PLoS ONE 17(8): e0270918. https://doi.org/10.1371/journal.pone.0270918 --------------------------------- 2- There is no mathematical or statistical analysis at all. Response: We have updated the manuscript to include Hausdorff Distance values, and some descriptive statistics of them. Reviewer again: Not addressed The authors can say that they didn't have any mathematical analysis since they are doing empirical research, then the editorial staff take their decision. 3- The methodology used in training, validation, and testing used in the model is unclear. Response: We tested the model using nested cross-validation. We do not have a separate validation set because we did not perform hyperparameter tuning. The method of nested cross-validation and the potential usefulness of hyperparameter tuning is described in the paper. Reviewer again: point Addressed 4- The authors should compare their results with related published references performing the segmentation task using the same data set without restriction themselves to the piglet MRI images. Reviewer again: Not addressed My opinion is: The comparison with the state-of-the-art is necessary or at least the authors should describe why their approach gives sufficient results that will e an added value to this research area without evidences!. Especially the tool used in this research (Mask R-CNN) is not new! ------------------- New sample comments: A- Line 17, the first sentence in the "Abstract" : Magnetic Resonance Imaging.... >>> Magnetic Resonance Imaging (MRI).... B- How can we evaluate the automated technique visually? Line 158 : "Visual inspection of the brain extractions reveals good accuracy of automatic brain extractions (Fig 3).... ****** 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. https://doi.org/10.1371/journal.pone.0284951.r004
Revision 2
21 Mar 2023 Author Response We have written our responses to the reviewers in the attached file, "Response to Reviewers". Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0284951.r005
13 Apr 2023 Decision Letter - Zhishun Wang, Editor Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks PONE-D-21-37916R2 Dear Dr. Larsen, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Zhishun Wang, Ph.D. 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: 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 Reviewer #2: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: No ****** 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 ****** 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: While the authors have addressed my comments for the most part, I am a little disappointed that the effort was not made to train another network for comparison. Training and testing another network for benchmarking is standard practice in image analysis and it does not require tremendous resources in the case of a U-net. The discussion comparing to the alternative U-net is ok, but it is a lesser replacement for head-to-head comparison. Reviewer #2: Almost all my previous comments in the previous review round have been addressed in this revised version. ****** 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: Yes: Ashraf A. M. Khalaf ******** https://doi.org/10.1371/journal.pone.0284951.r006
Formally Accepted
19 Apr 2023 Acceptance Letter - Zhishun Wang, Editor PONE-D-21-37916R2 Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks Dear Dr. Larsen: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Zhishun Wang Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0284951.r007

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