Deep spectral improvement for unsupervised image instance segmentation

Farnoosh Arefi; Amir M. Mansourian; Shohreh Kasaei

doi:10.1371/journal.pone.0307432

Peer Review History

Original SubmissionFebruary 13, 2024
9 May 2024 Decision Letter - Yawen Lu, Editor PONE-D-24-05072Deep Spectral Improvement for Unsupervised Image Instance SegmentationPLOS ONE Dear Dr. Kasaei, 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 Jun 23 2024 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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Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access. 4. Please ensure that you refer to Figure 11 and 12 in your text as, if accepted, production will need this reference to link the reader to the figure. 5. Please upload a copy of Supporting Information Figure/Table/etc. Supporting Information which you refer to in your text on page 15. Additional Editor Comments: PONE-D-24-05072 Dear Dr. Shohreh Kasaei: I am writing to you regarding the above referenced manuscript that you submitted to Plos One. Based on the enclosed two reviews, I am pleased to inform you that this manuscript is recommended for Major Revision in the journal. Please carefully address the reviewers' comments and suggestions regarding figure captions, more qualitative results, literature reviews, etc., to improve the quality of the manuscript in the revised submission. [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: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 ****** 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 ****** 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: The paper introduces a new method to improvise the performance of deep spectral methods specifically for instance segmentation. The authors incorporate two channel reduction modules to compare its effectiveness on the public datasets and validate the improvements in terms of mean IoU metric. The manuscript is complete and easily understandable, well experimented and contributes to new technical knowledge. I specifically liked the use of Bray-Curtis over Chebyshev instead of the conventional dot product for creating the affinity matrix. I suggest the manuscript to be accepted after a quick check for typographical errors and minor corrections stated below. The following are minor corrections that can be incorporated to improve quality: 1) In the captions of figures 8 and 9, the ”M” has both closing quotes and at many other places, this is a common mistake while using LaTeX and can be resolved with using (`) character. Please review similar typo throughout the manuscript. 2) I urge authors if they could extend and incorporate the experiments to few other image segmentation datasets so it is accepted by wider audience. This further validates the findings and enhances the quality of the paper. Reviewer #2: The article presents an interesting approach to improving deep spectral methods for the unsupervised instance segmentation task. The authors identify limitations in existing methods and propose novel techniques to address them. However, there are some aspects that should be improved. 1. The authors should strive for a more structured and logical flow in the proposed method section, ensuring the rationale and contributions are clear. 2. While the article includes some qualitative results, a more comprehensive visual analysis would greatly enhance the reader's understanding of the proposed method. 3. Although the authors have made efforts to include relevant literature in video and image segmentation, some are still missing in the related works section, such as [Coarse-to-fine video instance segmentation with factorized conditional appearance flows][Label-efficient video object segmentation with motion clues][Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation][Tripartite feature enhanced pyramid network for dense prediction]. 4. The writing quality and presentation of the article require improvement. ****** 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: 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.0307432.r001
Revision 1
27 May 2024 Author Response In relation to the review of the above mentioned manuscript, we would like to thank the respected anonymous reviewers for their valuable and constructive comments that led to improve the quality of the revised manuscript. We are pleased to see that the impact of the research in the field of deep spectral methods and instance segmentation has been acknowledged by the reviewers (R1, R2). We appreciate that the manuscript has been recognized as well-written and well-experimented (R1), and we are glad that it is considered novel in addressing the limitations of existing methods (R2). Furthermore, we are grateful that the importance of the proposed Bray-Curtis over Chebyshev (BoC) metric has been acknowledged (R1). We are also delighted to hear that R1 has found the manuscript as ready for acceptance after addressing the minor reviews. In the following, we have listed the issues raised by the respected reviewers and have addressed them as best as we could, and where appropriate we have revised the manuscript accordingly. Reviewer 1 The paper introduces a new method to improvise the performance of deep spectral methods specifically for instance segmentation. The authors incorporate two channel reduction modules to compare its effectiveness on the public datasets and validate the improvements in terms of mean IoU metric. The manuscript is complete and easily understandable, well experimented and contributes to new technical knowledge. I specifically liked the use of Bray-Curtis over Chebyshev instead of the conventional dot product for creating the affinity matrix. I suggest the manuscript to be accepted after a quick check for typographical errors and minor corrections stated below. 1) In the captions of figures 8 and 9, the ”M” has both closing quotes and at many other places, this is a common mistake while using LaTeX and can be resolved with using (`) character. Please review similar typo throughout the manuscript. We appreciate R1’s careful attention. It is edited in the revised version of the manuscript. 2) I urge authors if they could extend and incorporate the experiments to few other image segmentation datasets so it is accepted by wider audience. This further validates the findings and enhances the quality of the paper. We highly value the suggestion provided by R1 and have taken it into consideration by incorporating two additional datasets into our evaluation. For the Foreground-Background (Fg-Bg) segmentation task, we have extended the testing to include the Densely Annotated VIdeo Segmentation (DAVIS 2016) dataset, which is widely recognized as a challenging benchmark in the field. Moreover, for the instance segmentation task, we have reported our results on the Occluded Video Instance Segmentation (OVIS 2022) dataset. This newly proposed benchmark is of great importance due to its occluded nature, which introduces additional challenges for accurate instance segmentation. Thanks to R1, we believe that reporting the results on these two challenging datasets has led to better show the effectiveness of the proposed method. Reviewer 2 The article presents an interesting approach to improving deep spectral methods for the unsupervised instance segmentation task. The authors identify limitations in existing methods and propose novel techniques to address them. However, there are some aspects that should be improved. 1) The authors should strive for a more structured and logical flow in the proposed method section, ensuring the rationale and contributions are clear. We apologize if the previous text was unclear. We have made changes in the revised version to address this issue by providing additional explanations where necessary and removing the redundant parts. Furthermore, we have incorporated a new paragraph at the beginning of the proposed method section, which outlines the logical flow of our approach. This addition aims to assist readers in understanding the proper sequence of the proposed method. We are grateful to R2 for providing this feedback that let to improving the clarity of the manuscript. 2) While the article includes some qualitative results, a more comprehensive visual analysis would greatly enhance the reader's understanding of the proposed method. We completely understand R2's concern. In response, we have included a new figure (Figure 14), that showcases the outputs of the proposed method on the images from the newly added OVIS dataset. This contains an ablation study of the proposed BoC metric. It compares the outputs of the Bray Curtis, Chebyshev, and BoC metrics with that of the dot product and ground truth. The purpose of this visual comparison is to demonstrate the significance and necessity of the proposed BoC metric. Additionally, we have provided corresponding explanations for this figure in the Experimental Results section. We firmly believe that this additional visual analysis significantly enhances the reader's understanding of the underlying concepts and ideas behind the proposed method. 3) Although the authors have made efforts to include relevant literature in video and image segmentation, some are still missing in the related works section, such as [Coarse-to-fine video instance segmentation with factorized conditional appearance flows][Label-efficient video object segmentation with motion clues][Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation][Tripartite feature enhanced pyramid network for dense prediction]. We express our gratitude to R2 for providing this detailed feedback. We have thoroughly explored all the mentioned papers, as they are influential works recently published in top venues. Following careful examination, we have identified the most relevant papers that align with this research and have appropriately cited them in the relevant sections of the revised version. 4) The writing quality and presentation of the article require improvement. Thanks to R2, we have dedicated significant effort to revising the paper for multiple times with the aim of enhancing its readability. Additionally, we sought input from colleagues, who reviewed the paper and provided feedback on any ambiguous sections. As a result of these revisions and consultations, we believe that the revised version has significantly improved in terms of writing quality and presentation. We hope that the concerns raised by R2 have been effectively addressed. Attachments Attachment Submitted filename: Response Letter.pdf https://doi.org/10.1371/journal.pone.0307432.r002
17 Jun 2024 Decision Letter - Yawen Lu, Editor Deep Spectral Improvement for Unsupervised Image Instance Segmentation PONE-D-24-05072R1 Dear Dr. Shohreh Kasaei, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. 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Please follow the following steps and provide a camera-ready version of your manuscript. Congratulation! 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 ******** 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 ****** 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 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 ****** 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 ****** 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 all the comments including experiments with additional datasets and the manuscript can be accepted in present form. ****** 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 ******** https://doi.org/10.1371/journal.pone.0307432.r003
Formally Accepted
10 Jul 2024 Acceptance Letter - Yawen Lu, Editor PONE-D-24-05072R1 PLOS ONE Dear Dr. Kasaei, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, 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 customercare@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. Yawen Lu Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0307432.r004

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