{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:15:51Z","timestamp":1776266151004,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009852","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000}}],"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["DC20AC00003"],"award-info":[{"award-number":["DC20AC00003"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R25MD010391"],"award-info":[{"award-number":["R25MD010391"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R25MD010399"],"award-info":[{"award-number":["R25MD010399"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH-1U54HG007990"],"award-info":[{"award-number":["NIH-1U54HG007990"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics \u2014such as wound size progress\u2014 relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009852","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T13:42:27Z","timestamp":1647006147000},"page":"e1009852","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":53,"title":["Automatic wound detection and size estimation using deep learning algorithms"],"prefix":"10.1371","volume":"18","author":[{"given":"H\u00e9ctor","family":"Carri\u00f3n","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4720-6159","authenticated-orcid":true,"given":"Mohammad","family":"Jafari","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9639-5654","authenticated-orcid":true,"given":"Michelle Dawn","family":"Bagood","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5325-494X","authenticated-orcid":true,"given":"Hsin-ya","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7813-0858","authenticated-orcid":true,"given":"Roslyn Rivkah","family":"Isseroff","sequence":"additional","affiliation":[]},{"given":"Marcella","family":"Gomez","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"pcbi.1009852.ref001","doi-asserted-by":"crossref","unstructured":"Lucas Y, Niri R, Treuillet S, Douzi H, CASTANEDA BA Wound Size Imaging: Ready for Smart Assessment and Monitoring. Advances in Wound Care. 2020 Apr 22(ja).","DOI":"10.1089\/wound.2018.0937"},{"issue":"2","key":"pcbi.1009852.ref002","first-page":"51","article-title":"Wound assessment part 1: how to measure a wound","volume":"10","author":"EL Nichols","year":"2015","journal-title":"Wound Essentials"},{"issue":"2","key":"pcbi.1009852.ref003","first-page":"60","article-title":"The importance of continuous wound measuring","volume":"2","author":"G Gethin","year":"2006","journal-title":"WOUNDS UK"},{"issue":"6","key":"pcbi.1009852.ref004","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.2337\/diacare.26.6.1879","article-title":"Percent change in wound area of diabetic foot ulcers over a 4-week period is a robust predictor of complete healing in a 12-week prospective trial","volume":"26","author":"P Sheehan","year":"2003","journal-title":"Diabetes care"},{"key":"pcbi.1009852.ref005","unstructured":"ARANZMedical. Silhouette. [Internet] 2020 [Online, Date Accessed: 11\/09\/2020]. Available from: https:\/\/www.aranzmedical.com\/"},{"key":"pcbi.1009852.ref006","unstructured":"Woundworks. Woundworks inSight. [Internet] 2020 [Online, Date Accessed: 11\/09\/2020]. Available from: https:\/\/woundworks.com\/"},{"key":"pcbi.1009852.ref007","unstructured":"Healogics. Healogics Photo+3. [Internet] 2020 [Online, Date Accessed: 11\/09\/2020]. Available from: https:\/\/www.healogics.com\/woundsuite-wound-care-software\/wound-measurement-app\/"},{"issue":"2","key":"pcbi.1009852.ref008","first-page":"62","article-title":"Ten top tips for taking high-quality digital images of wounds","volume":"9","author":"BE Sperring","year":"2014","journal-title":"Wound Essentials"},{"issue":"3","key":"pcbi.1009852.ref009","doi-asserted-by":"crossref","first-page":"e1004845","DOI":"10.1371\/journal.pcbi.1004845","article-title":"Deep learning for population genetic inference","volume":"12","author":"S Sheehan","year":"2016","journal-title":"PLoS computational biology"},{"key":"pcbi.1009852.ref010","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. 234-241). Springer, Cham.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"11","key":"pcbi.1009852.ref011","doi-asserted-by":"crossref","first-page":"e1005177","DOI":"10.1371\/journal.pcbi.1005177","article-title":"Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments","volume":"12","author":"DA Van Valen","year":"2016","journal-title":"PLoS computational biology"},{"issue":"4","key":"pcbi.1009852.ref012","doi-asserted-by":"crossref","first-page":"e1007673","DOI":"10.1371\/journal.pcbi.1007673","article-title":"DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning","volume":"16","author":"JB Lugagne","year":"2020","journal-title":"PLoS computational biology"},{"issue":"8","key":"pcbi.1009852.ref013","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully convolutional architectures for multiclass segmentation in chest radiographs","volume":"37","author":"AA Novikov","year":"2018","journal-title":"IEEE transactions on medical imaging"},{"key":"pcbi.1009852.ref014","doi-asserted-by":"crossref","unstructured":"Shenoy VN, Foster E, Aalami L, Majeed B, Aalami O. Deepwound: automated postoperative wound assessment and surgical site surveillance through convolutional neural networks. In2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2018 Dec 3 (pp. 1017-1021). IEEE.","DOI":"10.1109\/BIBM.2018.8621130"},{"issue":"10","key":"pcbi.1009852.ref015","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1016\/j.jid.2018.05.014","article-title":"Interpretation of the Outputs of a Deep Learning Model Trained with a Skin Cancer Dataset","volume":"138","author":"SS Han","year":"2018","journal-title":"The Journal of investigative dermatology"},{"issue":"10","key":"pcbi.1009852.ref016","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1016\/j.jid.2018.06.175","article-title":"Automated classification of skin lesions: from pixels to practice","volume":"138","author":"A Narla","year":"2018","journal-title":"Journal of Investigative Dermatology"},{"issue":"3","key":"pcbi.1009852.ref017","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.jid.2019.12.029","article-title":"Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images","volume":"140","author":"M Cullell-Dalmau","year":"2020","journal-title":"Journal of Investigative Dermatology"},{"key":"pcbi.1009852.ref018","article-title":"Artificial intelligence in dermatology: a primer","author":"AT Young","year":"2020","journal-title":"Journal of Investigative Dermatology"},{"issue":"1","key":"pcbi.1009852.ref019","doi-asserted-by":"crossref","first-page":"e16","DOI":"10.2196\/iproc.4703","article-title":"Design of A Smartphone Application for Automated Wound Measurements for Home Care","volume":"1","author":"J Budman","year":"2015","journal-title":"Iproceedings"},{"issue":"9","key":"pcbi.1009852.ref020","doi-asserted-by":"crossref","first-page":"e0163092","DOI":"10.1371\/journal.pone.0163092","article-title":"Sequential change of wound calculated by image analysis using a color patch method during a secondary intention healing","volume":"11","author":"S Yang","year":"2016","journal-title":"PloS one"},{"issue":"1","key":"pcbi.1009852.ref021","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1186\/s12859-019-3308-1","article-title":"Wound area measurement with 3D transformation and smartphone images","volume":"20","author":"C Liu","year":"2019","journal-title":"BMC bioinformatics"},{"key":"pcbi.1009852.ref022","doi-asserted-by":"crossref","first-page":"105376","DOI":"10.1016\/j.cmpb.2020.105376","article-title":"Segmenting skin ulcers and measuring the wound area using deep convolutional networks","volume":"191","author":"DY Chino","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"pcbi.1009852.ref023","unstructured":"Wang C, Yan X, Smith M, Kochhar K, Rubin M, Warren SM, et al. 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IEEE."},{"issue":"1","key":"pcbi.1009852.ref024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-78799-w","article-title":"Fully automatic wound segmentation with deep convolutional neural networks","volume":"10","author":"C Wang","year":"2020","journal-title":"Scientific Reports"},{"issue":"4","key":"pcbi.1009852.ref025","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/JBHI.2018.2868656","article-title":"Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices","volume":"23","author":"M Goyal","year":"2018","journal-title":"IEEE journal of biomedical and health informatics"},{"issue":"3","key":"pcbi.1009852.ref026","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1049\/tje2.12016","article-title":"Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network","volume":"2021","author":"S Sarp","year":"2021","journal-title":"The Journal of Engineering"},{"key":"pcbi.1009852.ref027","doi-asserted-by":"crossref","unstructured":"Wagh A, Jain S, Mukherjee A, Agu E, Pedersen P, Strong D, et al. 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