DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas

Henrik Mustonen; Antti Isosalo; Minna Nortunen; Mika Nevalainen; Miika T. Nieminen; Heikki Huhta

doi:10.1371/journal.pone.0313126

Peer Review History

Original SubmissionJune 12, 2024
7 Aug 2024 Decision Letter - Hadeel K. Aljobouri, Editor PONE-D-24-21753DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: pancreasPLOS ONE Dear Dr. Mustonen, 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 21 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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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. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. Additional Editor Comments: The conclusion section must be added and separated from the discussion section. [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: This study introduces an annotation tool utilizing convolutional neural networks to enhance image analysis in medical research called DLLabelsCT. It aims to significantly reduce the time and labor needed for annotating medical images. The authors used pancreas CT scans as data. The study demonstrates DLLabelsCT's speed and accuracy, making it valuable for large datasets and adaptable for different organs. The authors compared their results with a modified MATLAB-based CTAnnotationTool, showing that DLLabelsCT is accurate, time-efficient, and resource-saving. Additionally, the authors provided all necessary libraries and packages, ensuring reproducibility for future research. This study is very impressive. I have a couple of comments: • The authors mentioned that in literature there are few annotation tools that support DL methods (Introduction second paragraph). It would be helpful to include more information about the existing methods, such as the models they used, their performance, and the size and type of datasets they trained on. • A flow chart of the whole process would be very useful, especially for understanding the datasets used. For instance, the authors have training, validation, and testing datasets consisting of different sources. Creating a visualization that shows which data was used for training before medical professionals' review, and which dataset was used afterward, would make the study much clearer. • The comparison with CTAnnotationTool regarding time and resources is very useful (Table 4, and 5). However, it would be beneficial to include performance metrics for CTAnnotationTool such as recall and precision. This would provide a clearer demonstration of DLLabelsCT's superior speed and also performance compared to CTAnnotationTool. Reviewer #2: dear authers in the section (materials) you mentioneed the chosen image size of 512 by 512, it is advised to provide in short the logic behind this size. in the section (training dataset) the scanning systems mentioned in the study have different manufacturer, what effect does this has on the aqcuired data and how does this eventually affect the model? in the section (validation dataset) the dataset from Oulu university was explained stating that 218 scans were cancerous, what methods of diagnosis were used? in the section (model training) it is mentioned that augmentation was used, how did this change the results? in the same section it is specified that a number of non pancrease containing slices were eleminated to enhance the time factor, could you please specify the amount in time redcuction if possible, what was the time consumed before eliminating the slices and what time was consumed after? ****** 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: Aya Abdul Salam Alsalihi ******** [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/. 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Revision 1
18 Sep 2024 Author Response Academic editor’s comments: Journal Requirements: 1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. 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 Author response: Thank you for your comment, the manuscript will be changed to meet these requirements. 2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. Author response: We agree with this, and have already made the annotation tool’s code open-source and available in https://github.com/MIPT-Oulu/DLLabelsCT 3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. Author response: There seems to have been a misunderstanding during the initial submission, this will be fixed in the resubmission. Additional Editor Comments: The conclusion section must be added and separated from the discussion section. Author response: This will be done in the resubmission. Reviewer #1: This study introduces an annotation tool utilizing convolutional neural networks to enhance image analysis in medical research called DLLabelsCT. It aims to significantly reduce the time and labor needed for annotating medical images. The authors used pancreas CT scans as data. The study demonstrates DLLabelsCT's speed and accuracy, making it valuable for large datasets and adaptable for different organs. The authors compared their results with a modified MATLAB-based CTAnnotationTool, showing that DLLabelsCT is accurate, time-efficient, and resource-saving. Additionally, the authors provided all necessary libraries and packages, ensuring reproducibility for future research. This study is very impressive. I have a couple of comments: • The authors mentioned that in literature there are few annotation tools that support DL methods (Introduction second paragraph). It would be helpful to include more information about the existing methods, such as the models they used, their performance, and the size and type of datasets they trained on. Author response: It is true that our article does not give much information about other annotation tools with deep learning capabilities. However, the articles about those tools focused more on the tool itself, instead of the deep learning model and their performance, meaning that such a comparison is not possible. Our manuscript focuses more on showing that a deep learning model can be trained to assist in annotating with a small open access dataset (Pancreas-CT in our case). Following changes were made to text, page 3 introduction section: “However, Philbrick et al. concentrated primarily on the mechanical aspects of the annotation tool, rather than illustrating how a deep learning model could be trained to aid in the annotation process.” • A flow chart of the whole process would be very useful, especially for understanding the datasets used. For instance, the authors have training, validation, and testing datasets consisting of different sources. Creating a visualization that shows which data was used for training before medical professionals' review, and which dataset was used afterward, would make the study much clearer. Author response: Thank you for your comment, we agree that such a flow chart would be useful and have added it into the manuscript as Figure 2 • The comparison with CTAnnotationTool regarding time and resources is very useful (Table 4, and 5). However, it would be beneficial to include performance metrics for CTAnnotationTool such as recall and precision. This would provide a clearer demonstration of DLLabelsCT's superior speed and also performance compared to CTAnnotationTool. Author response: We agree with the referee that including metrics for the CTAnnotationTool would be demonstrative. However, since the CTAnnotationTool lacks machine learning capabilities, it is not feasible to provide such performance metrics. Reviewer #2: dear authers in the section (materials) you mentioneed the chosen image size of 512 by 512, it is advised to provide in short the logic behind this size. Author response: Thank you for your comment, this image size is the size of the CT –scans, there were no other options. in the section (training dataset) the scanning systems mentioned in the study have different manufacturer, what effect does this has on the aqcuired data and how does this eventually affect the model? Author response: Referee pointed out an excellent detail. Generally, CT scans taken with different CT scanners provide some information to the model about how varied the imaging can be, with different pixel spacings and slight variations in grayscale values, etc. This eventually leads to the model better responding to those variations, increasing the segmentation quality. in the section (validation dataset) the dataset from Oulu university was explained stating that 218 scans were cancerous, what methods of diagnosis were used? Author response: Thank you for your comment, all scans from the patients with cancer imaging were later treated with surgery, and the diagnosis was based on the pathological examination of surgical specimens in the section (model training) it is mentioned that augmentation was used, how did this change the results? Author response: This is a good comment, augmentations have been proven to be an effective way to reduce model overfitting during training, especially on smaller datasets. This means that the model responds better to variations in the dataset and the model provides better quality segmentations on images that it has not seen. in the same section it is specified that a number of non pancrease containing slices were eleminated to enhance the time factor, could you please specify the amount in time redcuction if possible, what was the time consumed before eliminating the slices and what time was consumed after? Author response: Thank you for your comment, the datasets (Pancreas-CT and Oulu validation dataset) contained a total of 115,371 axial slices. The initial training dataset contained 18,942 axial slices and the model training took 24 hours. If all of those 115,371 axial slices were used during training, the model training would take around a week. This would’ve been too long for us, which means that the number of slices had to be reduced. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0313126.r002
2 Oct 2024 Decision Letter - Hadeel K. Aljobouri, Editor PONE-D-24-21753R1DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: pancreasPLOS ONE Dear Dr. Mustonen, 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 Nov 16 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. 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, Hadeel K. Aljobouri 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. Additional Editor Comments: The number of references is very small, we prefer to add more. I recommend the authors to read these papers: A. A. Alsalihi, H. K. Aljobouri, and E. A. K. ALTameemi, “GLCM and CNN Deep Learning Model for Improved MRI Breast Tumors Detection,” International Journal of Online and Biomedical Engineering (iJOE), vol. 18, no. 12, pp. 123–137, Sep. 2022, doi: 10.3991/IJOE.V18I12.31897. J. Yahya Rbat, H. K. Aljobouri, and A. M. Hasan, “MRI brain tumor classification using robust Convolutional Neural Network CNN approach,” pp. 258–261, Jan. 2023, doi: 10.1109/IICCIT55816.2022.10009913. N. H. Alkurdy, H. K. Aljobouri, and Z. K. Wadi, “Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 3, pp. 3440–3448, Jun. 2023, doi: 10.11591/IJECE.V13I3.PP3440-3448. J. F. Abdulkareem and H. K. Aljobouri, “Chest CT Images Analysis with Deep Learning Algorithms for COVID-19 Diagnostic for Iraqi Center,” AIP Conference Proceedings, vol. 2414, no. 1, Feb. 2023, doi: 10.1063/5.0117655/2870529. Z. K. Alkordy, N.H., Aljobouri, H.K. and Wadi, “Feature Extraction and Selection of Kidney Ultrasound Images Using a Deep CNN and PCA,” Proceedings of 6th Computational Methods in Systems and Software 2022, vol. 1, pp. 104–114, 2023, doi: DOI: 10.1007/978-3-031-21435-6_10. S. M. Alnedawe and H. K. Aljobouri, “A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches,” Baghdad Science Journal, vol. 20, no. 4, pp. 1402–1402, Aug. 2023, doi: 10.21123/BSJ.2023.7403. J. Y. R. Al-Awadi, H. K. Aljobouri, and A. M. Hasan, “MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM,” International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 02, pp. 134–149, Feb. 2023, doi: 10.3991/IJOE.V19I02.33987. A. M. Hasan, N. K. N. Al-Waely, H. K. Ajobouri, R. W. Ibrahim, H. A. Jalab, and F. Meziane, “A classification model of breast masses in DCE-MRI using kinetic curves features with quantum-Raina’s polynomial based fusion,” Biomedical Signal Processing and Control, vol. 84, p. 105002, Jul. 2023, doi: 10.1016/J.BSPC.2023.105002. A. M. Hasan, N. K. N. Al-Waely, H. K. Aljobouri, H. A. Jalab, R. W. Ibrahim, and F. Meziane, “Molecular subtypes classification of breast cancer in DCE-MRI using deep features,” Expert Systems with Applications, vol. 236, p. 121371, Feb. 2024, doi: 10.1016/J.ESWA.2023.121371. A. M. Hasan, H. K. Aljobouri, N. K. N. Al-Waely, R. W. Ibrahim, H. A. Jalab, and F. Meziane, “Diagnosis of breast cancer based on hybrid features extraction in dynamic contrast enhanced magnetic resonance imaging,” Neural Computing and Applications, pp. 1–14, Aug. 2023, doi: 10.1007/S00521-023-08909-Y/METRICS. N. B. Khalaf, H. K. Aljobouri, and M. S. Najim, “Identification and Classification of Retinal Diseases by Using Deep Learning Models,” in 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023, 2023, doi: 10.1109/SmartNets58706.2023.10215740. However, there is no need to cite the paper. [Note: HTML markup is below. Please do not edit.] [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.0313126.r003
Revision 2
17 Oct 2024 Author Response Academic editor’s comments: 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. Author response: We have gone through the references and have not found any papers that have been retracted. We have updated the References list to be more consistent and added a few more references. These references are number [3], [14] and [20] in the manuscript. Additional Editor Comments: The number of references is very small, we prefer to add more. Author response: We disagree with the comment regarding the low number of references. The 31 references in the original research article are adequate, and adding more references does not diminish the impact of this article. However, as the editor has requested more references, we have added three additional references to the manuscript. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0313126.r004
21 Oct 2024 Decision Letter - Hadeel K. Aljobouri, Editor DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: pancreas PONE-D-24-21753R2 Dear Dr. Mustonen, 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. If you have any questions relating to publication charges, 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, Hadeel K. Aljobouri Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0313126.r005
Formally Accepted
20 Nov 2024 Acceptance Letter - Hadeel K. Aljobouri, Editor PONE-D-24-21753R2 PLOS ONE Dear Dr. Mustonen, 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 Asst.Prof.Dr. Hadeel K. Aljobouri Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0313126.r006

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