CMRSegTools: An open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images

William A. Romero R.; Magalie Viallon; Joël Spaltenstein; Lorena Petrusca; Olivier Bernard; Loïc Belle; Patrick Clarysse; Pierre Croisille

doi:10.1371/journal.pone.0274491

Peer Review History

Original SubmissionMarch 15, 2022
21 Apr 2022 Decision Letter - Kumaradevan Punithakumar, Editor PONE-D-22-07768CMRSegTools: an open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR imagesPLOS ONE Dear Dr. Romero R., 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 05 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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If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you [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: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A 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 presents an open-source software for segmentation of CMR-LGE images. Quantitative LGE analysis is not strictly required by current CMR guidelines in the clinical setting (J Schulz-Menger JCMR 2020 10.1186/s12968-020-00610-6), but it is needed in clinical and translational research. A large number of LGE segmentation algorithms was proposed (Wu Front. Physiol 2021 \| 10.3389/fphys.2021.709230), few of them established in the clinical practice. About available software, several certified, commercial tools for CMR-LGE analysis are available at high cost from several companies. Some groups developed software for internal use but in my knowledge the only available free tools is the Segment software from Medvisio (https://medviso.com/cmr/) running by a free available Matlab VM on about all SO platforms. Segment is free usable if proper referencing is assured (https://medviso.com/how-to-refer/) but source code is not available, so the software cannot be adapted to user needs. Hence, the introduction of on open-source software is important and may represent an important improvement in the field. I tried the demo version in Horos and it seems to work well. Basic video tutorials are provided, maybe a manual would be also useful. Collaboration terms required for the use in scientific publication of the software (likely the only possible use as the software is of course not certified) are proper referencing as usually requested in the field and mandatory co-authorship of developers. The last request is not usual in open-source software sharing as co-authorship is usually applied id a specific developing work is done for a particular project. I guess that the GNU/CeCILL license doesn’t allow the developer to add further limitations with respect to the license itself. Is also not clear how this “co-authorship license” should apply if thanks to the open-source approach the software will be improved or modified by other developers external to the starting team. The “co-authorship license” constraint, if maintained, should be declared in the paper as well as it represents a significant limitation. Validation of the software is mostly anecdotic as single cases as presented without quantitative assessment. Is strongly advisable to perform a comparison with a validated software or manual analysis by an expert user on a reference data set including a reasonable number of cases, as MICCAI challenges or similar. Table 1 compares general-use software (that of course does not match R1/R2 requirements) with CMR specific ones that of course match both as required by SCMR guidelines. Research/Commercial Segment software should be inserted in the table together with other commercial software as Pie CAAS (https://www.piemedicalimaging.com/product/mr-solutions/tissue-characterisation). The compatibility with different OS should be indicated, as one limitation of the proposed software is the requirement of MacOS system. Reviewer #2: This study presents a open-source software for myocardial infarction segmentation in CMR images. There are some concerns: - It is better to brief introduce the licenses shown in Table 1 for clarifying the difference in different software. - Is there any comparison of BEAS with other myocardial contour segmentation ? If not, please explain the reason. - It is better to cite more studies in cardiac and myocardial infarction segmentation. 1) Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture, Medical Image Analysis; 2) Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences, Medical Image Analysis. - Please briefly discuss the limitation of this software - A lot of grammatical errors. - Please present a figure to show the modules and their connections in this software. ****** 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.0274491.r001
Revision 1
28 Jun 2022 Author Response Reviewer #1 1. The paper presents an open-source software for segmentation of CMR-LGE images. Quantitative LGE analysis is not strictly required by current CMR guidelines in the clinical setting (J Schulz-Menger JCMR 2020 10.1186/s12968-020-00610-6), but it is needed in clinical and translational research. A large number of LGE segmentation algorithms was proposed (Wu Front. Physiol 2021 \| 10.3389/fphys.2021.709230), few of them established in the clinical practice. We thank the reviewer for the observation and references. We have included the latter reference in our bibliographical revision and discussion. The following paragraph has been added to the Introduction: For instance, a recent publication of Wu et al. [5] presents a general review of cardiac scar segmentation methods (including methods for non-LGE images). This review reports quantitative results across publications. The conclusion states the need of a public benchmarking of the methods as a fairer review of their performances. This is a challenge that requires tools for open collaboration in a growing culture focused on advancing methods by reproducible science [6, 7]. The neuroimaging research community is a clear example of this research and development methodology [8, 9]. The following sentence has been also added to the Introduction: These approaches require annotated data sets which are generated and assessed by standard methods (i.e. signal threshold versus reference mean, full-width at half-maximum, etc.) [4, 5, 19, 20] The following reference has been added: 5. Wu, Yinzhe, Zeyu Tang, Binghuan Li, David Firmin, and Guang Yang. “Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.” Frontiers in Physiology 12 (2021). 2. About available software, several certified, commercial tools for CMR-LGE analysis are available at high cost from several companies. Some groups developed software for internal use but in my knowledge the only available free tools is the Segment software from Medvisio(https://medviso.com/cmr/) running by a free available Matlab VM on about all SO platforms. Segment is free usable if proper referencing is assured (https://medviso.com/how-to-refer/) but source code is not available, so the software cannot be adapted to user needs. Hence, the introduction of on open-source software is important and may represent an important improvement in the field. I tried the demo version in Horos and it seems to work well. Basic video tutorials are provided, maybe a manual would be also useful. Collaboration terms required for the use in scientific publication of the software (likely the only possible use as the software is of course not certified) are proper referencing as usually requested in the field and mandatory co-authorship of developers. The last request is not usual in open-source software sharing as co-authorship is usually applied id a specific developing work is done for a particular project. I guess that the GNU/CeCILL license doesn’t allow the developer to add further limitations with respect to the license itself. Is also not clear how this “co-authorship license” should apply if thanks to the open-source approach the software will be improved or modified by other developers external to the starting team. The “co-authorship license” constraint, if maintained, should be declared in the paper as well as it represents a significant limitation. We thank the reviewer for testing our application software and pointing out the potential limitations of the use of CMRSegTools under the current “co-authorship licence”. Before releasing CMRSegTools as an open-source software, we provided a binary version of the plug-in as well as scientific expertise in applied computing in Cardiac MRI and software customisation to the particular needs of the users. This makes us active collaborators in the research projects using the application software. In this context, the co-authorship agreement has been working among research teams using CMRSegTools. Now our interest is to join efforts through an open collaboration. Releasing the source code of CMRSegTools together with this publication is our first step. The GNU/CeCILL is the French free software license, compatible with the GNU GPL, the license grants access to the source code and the rights to copy, modify and redistribute the code. We have also included Segment CMR (https://medviso.com/cmr) and Cass MR Solutions (https://www.piemedicalimaging.com/product/mr-solutions) in our software comparison. The sentence below has been added to the Discussion section: In this direction, CMRSegTools source code has been released under CeCILL licence (French free software license compatible with GPL) that grants users the right to copy, modify, and distribute new versions of the plug-in. 3. Validation of the software is mostly anecdotic as single cases as presented without quantitative assessment. Is strongly advisable to perform a comparison with a validated software or manual analysis by an expert user on a reference data set including a reasonable number of cases, as MICCAI challenges or similar. We thank the reviewer for this observation. The software has already been tested before this open-source release. All implemented methods have been evaluated in the publications cited in appendix S1. The following paragraph has been added to the CMRSegTools plug-in section: BEAS was originally developed for automatic segmentation of 3D cine-loop echocardiographic images to provide an efficient, fast and accurate solution for quantification of the main left ventricular volumetric indices used in clinical routine. In addition, the method was adapted for 3D+time CMR data sets (acquired by a cine steady state free precession sequence, SSFP) and benchmarked against the data sets available from the MICCAI 2009 Cardiac MR Left Ventricle Segmentation Challenge [33]. The benchmark demonstrated the technique to be robust, efficient and fast in terms of accuracy and computational load, which makes the BEAS algorithm suitable for a clinical practice [32]. And in the Discussion section: To validate the correspondence between the mathematical model (S1 appendix) and the code implementation, the correctness of the computational methods has been verified with synthetic data. An extended assessment or benchmarking of infarct segmentation methods is beyond the scope of this paper. The goal is to provide a computer-based tool which includes well-known lesion segmentation methods (S1 appendix) in order to empower research scientists to reproduce results and assess comparatively new methods. In this direction, CMRSegTools source code has been released under CeCILL licence (French free software license compatible with GPL) that grants users the right to copy, modify, and distribute new versions of the plug-in. 4. Table 1 compares general-use software (that of course does not match R1/R2 requirements) with CMR specific ones that of course match both as required by SCMR guidelines. Research/Commercial Segment software should be inserted in the table together with other commercial software as Pie CAAS (https://www.piemedicalimaging.com/product/mr-solutions/tissue-characterisation). The compatibility with different OS should be indicated, as one limitation of the proposed software is the requirement of MacOS system. We thank the reviewer for this recommendation. We have included Segment CMR and CAAS MR Solutions to the software comparison. In addition, we have included a column “Target Platform” in Table 1. We have added comments on the limitation to deploy CMRSegTools on a different operative system. The Table 1 has been completed. And a paragraph added to the Discussion section: CMRSegTools is therefore a contribution to enable reproducible research by assembling the most used methods for infarct segmentation in a suitable workflow executed in a single and widely available environment: Osirix/Horos DICOM viewers. This platform was chosen because it provides an Application Programming Interface (API) that grants access to the data management, user interaction and visualisation functionalities. It was also chosen because it has an open-source licence. However, OsiriX/Horos is a macOS-only software, which limits its deployment on platforms based on a different operative system. Reviewer #2 This study presents a open-source software for myocardial infarction segmentation in CMR images. There are some concerns: 1. It is better to brief introduce the licenses shown in Table 1 for clarifying the difference in different software. We thank the reviewer for this recommendation. We have added a brief overview of licences in the Introduction: In general, commercial software is a product designed to be marketed under a utilisation licence. The user must pay for a binary version which must be used as described by the licence. There is no access to the source code of the commercial product (closed-source). Freeware software, is free of charge but closed-source. In open-source software, the user has access to the source code and the different licences (i.e. BSD, GNU GPL, etc.) provide the rights to copy, modify, redistribute the code, as well as build and release binary versions with commercial purposes. [11]. Open-source software promotes open collaboration and research communities have been attracted to this innovation methodology [12]. The following sentence has been added to the Discussion section: In this direction, CMRSegTools source code has been released under the CeCILL licence (French free software license compatible with GPL) that grants users the right to copy, modify, and distribute new versions of the plug-in. 2. Is there any comparison of BEAS with other myocardial contour segmentation ? If not, please explain the reason. We thank the reviewer for this important remark. In CMRSegTools plug-in section, the paragraph below has been added: BEAS was originally developed for automatic segmentation of 3D cine-loop echocardiographic images to provide an efficient, fast and accurate solution for quantification of the main left ventricular volumetric indices used in clinical routine. In addition, the method was adapted for 3D+time CMR data sets (acquired by a cine steady state free precession sequence, SSFP) and benchmarked against the data sets available from the MICCAI 2009 Cardiac MR Left Ventricle Segmentation Challenge [33]. The benchmark demonstrated the technique to be robust, efficient and fast in terms of accuracy and computational load, which makes the BEAS algorithm suitable for a clinical practice [32]. 3. It is better to cite more studies in cardiac and myocardial infarction segmentation. 1) Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture, Medical Image Analysis; 2) Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences, Medical Image Analysis. We thank the reviewer for the observation and references. We have included Xu C., et al. 2018 and Guo S. et al., 2021 in our bibliographical revision and discussion. In the Discussion section we added, At present, there are no automated algorithms that can reliably distinguish the bright LV cavity from the bright endocardial border of the infarct. This challenge is being addressed by new segmentation methods (including non-LGE images) based on ML [5,20, 47, 48]. In the References, we added 47. Xu C, Xu L, Gao Z, Zhao S, Zhang H, Zhang Y, et al. Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Medical image analysis. 2018;50:82–94. 48. Guo S, Xu L, Feng C, Xiong H, Gao Z, Zhang H. Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences. Medical Image Analysis. 2021;73:102170. 4. Please briefly discuss the limitation of this software In the Discussion section, the following comment has been added: CMRSegTools is therefore a contribution to enable reproducible research by assembling the most used methods for infarct segmentation in a suitable workflow executed in a single and widely available environment: Osirix/Horos DICOM viewers. This platform was chosen because it provides an Application Programming Interface (API) that grants access to the data management, user interaction and visualisation functionalities. It was also chosen because it has an open-source licence. However, OsiriX/Horos is a macOS-only software, which limits its deployment on platforms based on a different operative system. 5. A lot of grammatical errors. We have thoroughly reviewed and improved the grammar of the article. 6. Please present a figure to show the modules and their connections in this software. We thank the reviewer for this recommendation. We added the diagram below presenting the main components and package dependencies in the CMRSegTools plug-in section: Figure 1 shows the main components of the plug-in and its integration within the OsiriX/Horos environment. Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0274491.r002
16 Aug 2022 Decision Letter - Kumaradevan Punithakumar, Editor PONE-D-22-07768R1CMRSegTools: an open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR imagesPLOS ONE Dear Dr. Romero R., 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 Sep 30 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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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, Kumaradevan Punithakumar 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. 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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: N/A ****** 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: No ****** 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: Authors have addressed my requests. I have some very minor comments: radiology expert experience should be quantified (e.g. > 10 years) Reference 25 should be updated (pages 32(7):4340-4351) ****** 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 ******** [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.0274491.r003
Revision 2
19 Aug 2022 Author Response Response to reviewers Reviewer #1: Authors have addressed my requests. I have some very minor comments: 1. radiology expert experience should be quantified (e.g. > 10 years) We thank the reviewer for this important remark. The following paragraph has been edited in the section Radiology expert segmentation vs automated segmentation methods: An example of the manual segmentation made by an expert radiologist (P.C. with 20 years of experience in cardiovascular imaging) against each one of the methods within CMRSegTools on a patient with MVO is presented in figures 7 and 8. 2. Reference 25 should be updated (pages 32(7):4340-4351) We thank the reviewer for the observation. The reference has been updated and following the PLOS One reference style the reference 25 is written as follows: 25. Zeilinger MG, Kunze KP, Munoz C, Neji R, Schmidt M, Croisille P, et al. Non-rigid motion-corrected free-breathing 3D myocardial Dixon LGE imaging in a clinical setting. European Radiology. 2022;32. doi:10.1007/s00330-022-08560-6. Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0274491.r004
30 Aug 2022 Decision Letter - Kumaradevan Punithakumar, Editor CMRSegTools: an open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images PONE-D-22-07768R2 Dear Dr. Romero R., 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, Kumaradevan Punithakumar Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0274491.r005
Formally Accepted
5 Sep 2022 Acceptance Letter - Kumaradevan Punithakumar, Editor PONE-D-22-07768R2 CMRSegTools: an open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images Dear Dr. Romero R.: 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 Professor Kumaradevan Punithakumar Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0274491.r006

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