Robust cardiac segmentation corrected with heuristics

Alan Cervantes-Guzmán; Kyle McPherson; Jimena Olveres; Carlos Francisco Moreno-García; Fabián Torres Robles; Eyad Elyan; Boris Escalante-Ramírez

doi:10.1371/journal.pone.0293560

Peer Review History

Original SubmissionJune 28, 2023
31 Jul 2023 Decision Letter - Anas Bilal, Editor PONE-D-23-19397Robust cardiac segmentation corrected with heuristicsPLOS ONE Dear Dr. Escalante-Ramírez, 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 14 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. 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If you are working on all four chambers of heart, please clearly indicate its importance, why four chambers are more significant than only considering LV (this is most important part to diagnose cardiac diseases). 3. Fig 2 is not clear, before and after segmentation, images look same. 4. Line 215, figure reference is incorrect. 5. Why is normalization required? 6. On line 228 section numbers are not mentioned. 7. In table 2, why results are shown on selected images when they are shown for all images? 8. In discussion, explain the reason of having different results in figure 12,13, 14 and 15. 9. No comparison of results with any state-of-the-art models. 10. Also mention machine learning models and image processing models for cardiac segmentation and mention papers related to them in the literature review, for example “Two-stage active contour model for robust left ventricle segmentation in cardiac MRI”. In addition to this refer more non deep learning models as well for cardiac segmentation. Reviewer #2: In the manuscript “Robust cardiac segmentation corrected with heuristics” is presented a cardiac segmentation algorithm based on the U-NET architecture, with two additional steps to improve the quality of the results (a cone segmentation algorithm and a post-processing step. This manuscript addresses a topic of high interest and very current. The proposed approach complements a well-known model that has already been used by other authors, with two steps that, according to the results shown by the authors, improve the accuracy of the segmentations. In general, the paper is well written and well structured, being easy to read and understand. However, there are some points that I think can be improved, as well as some issues that need to be addressed. My comments are as follows: The authors claim that the proposed system implements a post-processing step that refines the shape and contours of the segmentation based on heuristics provided by physicians. However, from what I understood from the Heuristic Correction section, the correction method automatically identifies if a mask overlaps another one below/above and cuts the excess sections, based on a distance defined by the authors. In my opinion this needs a more detailed explanation. Although the "Related Work" section is very well structured and references work in the field, in my opinion it could be improved by including more recent work. As far as I could see, only 2 works from 2023 and 1 from 2022 are referenced, the rest being earlier. Figure 2 - To allow a faster understanding and analysis of this figure, the authors may consider identifying the images that compose it with (a), (b), ..., adding their summary description to the caption. For example (a) original image; (b) ... Figure 3 is difficult to parse. Namely, the legends for the arrows (bottom right corner) are not legible. Authors should consider its improvement. Table 1 should appear before “Heuristic Correction” section. The authors define distances from which they consider that the overlapping of the masks should be removed, considering that below these values the overlaps are considered normal. Why? These assumptions need to be better substantiated. Figures 4 and 5 are too far from the text where they are referred to and explained (with table 1 in the middle). They should be replaced. Figure 8 - The predicted masks are very different from those generated by the clinical expert. Do the authors consider this a good/precise segmentation? Minor comments: Line 6 - Please, remove "." in "specialists." Line 15 - Consider writing "... as good contrast as..." instead of "... as good a contrast as..." Line 21 - Please, consider writing "... diagnosis results. instead of "diagnosis performance." Line 215 - I think the authors refer to figure 7 and not figure 4. Lines 228 and 229 - Please revise "Section ??" Lines 77-80 - Please, revise the sentence "More recently, for 77 cardiac segmentation, a study by Dang et al. [28], [29], authors..." Line 303 - Consider writing "... the metrics decrease. We identified,,," instead of "... the metrics decrease, we identified..." ****** 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.0293560.r001
Revision 1
28 Sep 2023 Author Response Response to reviewers ############## Reviewer # 1 ############## 1. Abstract is not well written. ANSWER: Abstract has been corrected for style, clarity, and structure -------------------------- 2. If you are working on all four chambers of heart, please clearly indicate its importance, why four chambers are more significant than only considering LV (this is most important part to diagnose cardiac diseases). ANSWER: Dear reviewer, thank you for your concern, we added a paragraph indicating the importance of the four chambers segmentation at the end of the Introduction stating: "Furthermore, the American Society of Echocardiography and the European Association of Cardiovascular Imaging provide a set of guidelines for assessing measurements related to the four cardiac chambers. They state that these measurements are essential for evaluating cardiac function and extracting important clinical parameters". --------------------------- 3. Fig 2 is not clear, before and after segmentation, images look same. ANSWER: Thank you. The second reviewer made the same observation. We improved the Figure 2 for a more accurate and insightful interpretation --------------------------- 4. Line 215, figure reference is incorrect. ANSWER: Figure has been correctly referenced. --------------------------- 5. Why is normalization required? ANSWER: The batch normalization aids the model in achieving a smoother initialization, facilitating quicker training and even mitigating the issue of overfitting. This is explained in Section 3.2 ---------------------------- 6. On line 228 section numbers are not mentioned. ANSWER: This has been corrected, thank you for the observation. -------------------------------- 7. In table 2, why results are shown on selected images when they are shown for all images? ANSWER: Table 2 shows two sets of metric calculations. In columns 3 and 4, we show the dice coefficient average and the average change (in percent) for all images on the experiment (218) in total. Meanwhile, in columns 5 and 6 we show these averages but only for the images which the clinicians marked, and thus where the adjustment was really needed. This shows that even if the correction is not required, we can still obtain an upgrade between 0.4 and 2 percent. However, if the adjustment is requested by a human observer, these results can improve to 3 to 7 percent. We have modified the table footer and included the following text to clarify this point: "Similarly, we ran dice coefficient calculations for each of the four chamber masks. Once again, all dice coefficients were superior in all images of the sequence for the corrected masks compared to the original U-Net ones, as shown in columns 3 and 4 from Table 2. Furthermore, the differences were even better when only considering the images that were indicated to require a correction rather than the entire set (columns 5 and 6). In fact, the data of columns 5 and 6 corresponds only to images selected by a second clinician that was unsure of the prediction made by the model and then corrected with our heuristics-based algorithm. This shows that even if another clinician is unsure whether the correction should be applied or not, it is more likely that the correction yields better results rather than not applying it at all. Finally, notice that we had more success correcting masks from the right than those on the left side." ------------------------------ 8. In discussion, explain the reason of having different results in figure 12,13, 14 and 15. ANSWER: Indeed, the images correspond to two examples for every case. We apologize for the misunderstanding generated by figures 12,13,14 and 15, which is why we decided to rephrase the descriptions of each of these figures to make our intention clearer for each of these examples. ------------------------------- 9. No comparison of results with any state-of-the-art models. ANSWER: Dear reviewer, we appreciate your concern on this crucial aspect of our paper. We could not locate a state-of-the-art work that aligns with our approach. In other words, no article was found that segments four cardiac chambers in echocardiography images of adult patients. We found recent works on the segmentation of four chambers, but they focus on fetal echocardiography images, a scenario that is different than ours and is therefore not comparable. This was already pointed out in the introduction of our work (Line 29). ------------------------------- 10. Also mention machine learning models and image processing models for cardiac segmentation and mention papers related to them in the literature review, for example “Two-stage active contour model for robust left ventricle segmentation in cardiac MRI”. In addition to this refer more non deep learning models as well for cardiac segmentation. (Alan) ANSWER: Thank you very much for your suggestion. We have included a discussion on deformable model methods (Active Shape Models and Active Contour Models) at the beginning of Section 2. ############## Reviewer #2 # ############## 1. The authors claim that the proposed system implements a post-processing step that refines the shape and contours of the segmentation based on heuristics provided by physicians. However, from what I understood from the Heuristic Correction section, the correction method automatically identifies if a mask overlaps another one below/above and cuts the excess sections, based on a distance defined by the authors. In my opinion this needs a more detailed explanation. ANSWER: Indeed, the system consists of three phases 1) Cone segmentation 2) UNET for chamber segmentation and 3) Heuristics correction of the masks generated. Indeed, in the last step the method automatically identifies if there is a mask overlap based on some adjustable parameters, and also smoothens the masks to have more realistic outputs. We have enhanced this explanation through the following text on section 3.3: "To address this issue, we devised a heuristic-based corrective method to automatically detect whether any of the predicted masks overlap - either above or below the actual chamber(s) - and will clip off the overlapping area of the mask if it exceeds a pre-determined threshold value. Consider the example provided in Figure~\\ref{fig:overlap}. The method will apply a cut to the VD mask (chamber in pink) if the maximum $y$-axis value is greater than the minimum $y$-axis value of the chamber below (i.e. the violet mask labelled AD). To determine the value, we consider the Euclidean distance between the two most extreme points of the masks and apply a cut for distances greater than $68.5$ on the right masks (in this case, VD and AD) and greater than $76.5$ on the left masks (the green and purple ones labelled as AI and VI respectively). These thresholds were identified through experimenting with different values on the predicted masks prior to the method being automated in the system, which revealed that distances less than these would not benefit from being cut as the overlap is very minor, and thus should be ignored and assumed to be a regular overlap that conforms to the anatomical structure of the heart. In this example, the distance between both points is $d=\\sqrt{{(54-45)}^2+{(9-80)}^2}=71.6$ and will be cut as shown in Figure~\\ref{fig:overlap2}." --------------- 2. Although the "Related Work" section is very well structured and references work in the field, in my opinion it could be improved by including more recent work. As far as I could see, only 2 works from 2023 and 1 from 2022 are referenced, the rest being earlier. ANSWER: Thank you very much for the suggestion of including more up-to-date references. We are aware of the importance of providing a study that is current, especially in rapidly evolving technological subjects. We have added as many recent references as possible (from the year 2021 onwards) that also align with the necessary relevance to our work. ----------------- 3. Figure 2 - To allow a faster understanding and analysis of this figure, the authors may consider identifying the images that compose it with (a), (b), ..., adding their summary description to the caption. For example (a) original image; (b) ... ANSWER: We greatly appreciate your suggestion, which we gladly took and applied to Figure 2. -------------------- 4. Figure 3 is difficult to parse. Namely, the legends for the arrows (bottom right corner) are not legible. Authors should consider its improvement. ANSWER: Thank you for this observation, we chose to upscale the Figure 3 to make it more legible. -------------------- 5. Table 1 should appear before “Heuristic Correction” section. ANSWER: Thanks, the Table 1 is now in its right place (before "Heuristic Correction" section). --------------------- 6. The authors define distances from which they consider that the overlapping of the masks should be removed, considering that below these values, the overlaps are considered normal. Why? These assumptions need to be better substantiated. ANSWER: As mentioned in the response to the reviewer's first comment, we have enhanced this explanation in section 3.3 to address better why our assumptions are valid in the heart chamber segmentation scenario. ----------------------- 7. Figures 4 and 5 are too far from the text where they are referred to and explained (with table 1 in the middle). They should be replaced. ANSWER: Dear reviewer, we would like to express our gratitude for bringing attention to this details which has been rectified and appropriately positioned near of the text where they are referred. ----------------------- 8. Figure 8 - The predicted masks are very different from those generated by the clinical expert. Do the authors consider this a good/precise segmentation? ANSWER: The original images showed a case where our segmentation was not as accurate compared to the clinician's one. We have changed the image to a better segmentation, and added the following lines to explain the new Figure 9, which is the zoom version of Fig 8: Notice that the left ventricle and the right atrium segmentations have been smoothed (top and bottom circles respectively), while the overlap between ventricles and atriums has disappeared (left and right circles). ---------------------- ################## Minor comments: # ################## Line 6 - Please, remove "." in "specialists." ANSWER; Corrected, thanks. ------------------ Line 15 - Consider writing "... as good contrast as..." instead of "... as good a contrast as..." ANSWER; Corrected, thanks. ------------------ Line 21 - Please, consider writing "... diagnosis results. instead of "diagnosis performance." ANSWER; Corrected, thanks. ----------------- Line 215 - I think the authors refer to figure 7 and not figure 4. ANSWER: Thanks for spotting this. We have corrected the figure reference. ---------------- Lines 228 and 229 - Please revise "Section ??" ANSWER: Corrected, and we have also added section/subsection numbers ---------------- Lines 77-80 - Please, revise the sentence "More recently, for 77 cardiac segmentation, a study by Dang et al. [28], [29], authors..." ANSWER; Corrected, thanks. ----------------- Line 303 - Consider writing "... the metrics decrease. We identified,,," instead of "... the metrics decrease, we identified..." ANSWER; Corrected, thanks. Attachments Attachment Submitted filename: Response to reviewers.pdf https://doi.org/10.1371/journal.pone.0293560.r002
16 Oct 2023 Decision Letter - Anas Bilal, Editor Robust cardiac segmentation corrected with heuristics PONE-D-23-19397R1 Dear Dr. Ramírez, 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, Anas Bilal, Ph.D. Academic Editor PLOS ONE 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: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: (No Response) ****** 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) ****** 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) ****** 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: All concerns are addressed in the revised manuscript. I recommend this article for publication in this journal. Reviewer #2: (No Response) ****** 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: Yes: Maria tamoor Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0293560.r003
Formally Accepted
19 Oct 2023 Acceptance Letter - Anas Bilal, Editor PONE-D-23-19397R1 Robust Cardiac Segmentation Corrected with Heuristics Dear Dr. Escalante-Ramírez: 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. Anas Bilal Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0293560.r004

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