{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T09:30:29Z","timestamp":1768642229838,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Government of the Basque Country","award":["IT905-16 grant"],"award-info":[{"award-number":["IT905-16 grant"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.<\/jats:p>","DOI":"10.3390\/s20102933","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T11:31:18Z","timestamp":1590060678000},"page":"2933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Sofia","family":"Zahia","sequence":"first","affiliation":[{"name":"eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain"},{"name":"Computer Science and Engineering Department, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5274-8596","authenticated-orcid":false,"given":"Adel","family":"Elmaghraby","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University of Louisville, Louisville, KY 40292, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1074","DOI":"10.1097\/CCM.0000000000003366","article-title":"Incidence and prevalence of pressure injuries in adult intensive care patients: A systematic review and meta-analysis","volume":"46","author":"Chaboyer","year":"2018","journal-title":"Crit. Care Med."},{"key":"ref_2","unstructured":"Agency for Healthcare Research and Quality (AHRQ) (2020, March 24). 2013 Annual Hospital-Acquired Condition Rate and Estimates of Cost Savings and Deaths Averted From 2010 to 2013, Available online: https:\/\/www.ahrq.gov\/sites\/default\/files\/publications\/files\/hacrate2013_0.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101742","DOI":"10.1016\/j.artmed.2019.101742","article-title":"Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods","volume":"102","author":"Zahia","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_4","unstructured":"Agency for Healthcare Research and Quality (2020, March 28). Preventing Pressure Ulcers in Hospitals, Available online: https:\/\/www.ahrq.gov\/professionals\/systems\/hospital\/pressureulcertoolkit\/putool1.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jval.2017.07.007","article-title":"An economic evaluation of the impact, cost, and medicare policy implications of chronic nonhealing wounds","volume":"21","author":"Nussbaum","year":"2018","journal-title":"Value Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"261","DOI":"10.12968\/jowc.2012.21.6.261","article-title":"The cost of pressure ulcers in the United Kingdom","volume":"21","author":"Dealey","year":"2012","journal-title":"J. Wound Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.3390\/s120201437","article-title":"Accuracy and resolution of Kinect depth data for indoor mapping applications","volume":"12","author":"Khoshelham","year":"2012","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"568","DOI":"10.3390\/s90100568","article-title":"State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation","volume":"9","author":"Sansoni","year":"2009","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1007\/s11042-016-3949-2","article-title":"Development of an automatic 3D human head scanning-printing system","volume":"76","author":"Zhang","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MC.2007.225","article-title":"3D body scanning and healthcare applications","volume":"40","author":"Treleaven","year":"2007","journal-title":"Computer"},{"key":"ref_11","unstructured":"(2020, April 28). Structure Sensor. 3D Scanning, Mixed Reality and more, for any Device from iPads to Robots. Available online: https:\/\/structure.io\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/10095020.2016.1235817","article-title":"Accuracy and utility of the Structure Sensor for collecting 3D indoor information","volume":"19","author":"Kalantari","year":"2016","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_13","first-page":"52","article-title":"Wound measurement techniques: Comparing the use of ruler method, 2D imaging and 3D scanner","volume":"5","author":"Shah","year":"2013","journal-title":"J. Am. Coll. Clin. Wound Spec."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1109\/JBHI.2017.2743526","article-title":"Segmentation and measurement of chronic wounds for bioprinting","volume":"22","author":"Gholami","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1109\/TBME.2016.2632522","article-title":"Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification","volume":"64","author":"Wang","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, S., Paul, S., Rao, S.S., and Krishnareddy, A. (2015, January 2\u20134). Segmenting skin ulcers based on thresholding and watershed segmentation. Proceedings of the International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2015.7322805"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compbiomed.2015.02.015","article-title":"Computerized segmentation and measurement of chronic wound images","volume":"60","author":"Fauzi","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nandagopan, G.L., and Haripriya, A.B. (2016, January 6\u20138). Implementation and comparison of two image segmentation techniques on thermal foot images and detection of ulceration using asymmetry. Proceedings of the International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2016.7754155"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, H., Lee, B.U., Park, J., Sun, W., Oh, B., and Yang, S. (2015, January 28\u201330). Segmentation of wounds using gradient vector flow. Proceedings of the International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS.2015.7439552"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Haider, A., Alhashim, M., Tavakolian, K., and Fazel-Rezai, R. (2016, January 19\u201321). Computer-assisted image processing technique for tracking wound progress. Proceedings of the IEEE International Conference on Electro Information Technology (EIT), Grand Forks, ND, USA.","DOI":"10.1109\/EIT.2016.7535333"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1166\/jmihi.2013.1124","article-title":"Segmentation of chronic wound areas by clustering techniques using selected color space","volume":"3","author":"Yadav","year":"2013","journal-title":"J. Med. Imaging Health Inf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1111\/j.1600-0846.2009.00415.x","article-title":"Lower extremity ulcer image segmentation of visual and near-infrared imagery","volume":"16","author":"Bochko","year":"2010","journal-title":"Ski. Res. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Seixas, J.L., Barbon, S., and Mantovani, R.G. (2015, January 22\u201325). Pattern recognition of lower member skin ulcers in medical images with machine learning algorithms. Proceedings of the IEEE 28th International Symposium on Computer-Based Medical Systems, Sao Carlos, Brazil.","DOI":"10.1109\/CBMS.2015.48"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s11517-014-1240-0","article-title":"Efficient detection of wound-bed and peripheral skin with statistical colour models","volume":"53","author":"Veredas","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_28","unstructured":"Zhang, X., Yang, L., Wang, J., Zhao, Q., and Qiao, A. (July, January 29). The design of wound area measurement software based on Android operating system. Proceedings of the 11th World Congress on Intelligent Control and Automation, Shenyang, China."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Trabelsi, O., Tlig, L., Sayadi, M., and Fnaiech, F. (2013, January 21\u201323). Skin disease analysis and tracking based on image segmentation. Proceedings of the 2013 International Conference on Electrical Engineering and Software Applications, Hammamet, Tunisia.","DOI":"10.1109\/ICEESA.2013.6578486"},{"key":"ref_30","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":"Chino","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dorileo, \u00c9.A., Frade, M.A., Rangayyan, R.M., and Azevedo-Marques, P.M. (2010, January 2\u20135). Segmentation and analysis of the tissue composition of dermatological ulcers. Proceedings of the CCECE 2010, Calgary, AB, Canada.","DOI":"10.1109\/CCECE.2010.5575143"},{"key":"ref_32","first-page":"211","article-title":"Characterization and pattern recognition of color images of dermatological ulcers: A pilot study","volume":"22","author":"Pereyra","year":"2014","journal-title":"Comput. Sci. J. Mold."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Blanco, G., Bedo, M.V., Cazzolato, M.T., Santos, L.F., Jorge, A.E.S., Traina, C., Azevedo-Marques, P.M., and Traina, A.J. (2016, January 11\u201313). A label-scaled similarity measure for content-based image retrieval. Proceedings of the 2016 IEEE International Symposium on Multimedia (ISM), San Jose, CA, USA.","DOI":"10.1109\/ISM.2016.0014"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chino, D.Y.T., Scabora, L.C., Cazzolato, M.T., Jorge, A.E.S., Traina, C., and Traina, A.J.M. (2018, January 18\u201321). ICARUS: Retrieving skin ulcer images through bag-of-signatures. Proceedings of the 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden.","DOI":"10.1109\/CBMS.2018.00022"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s12938-016-0298-3","article-title":"Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry","volume":"16","author":"Ortiz","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"(2020, May 10). PyTorch3D. Available online: https:\/\/github.com\/facebookresearch\/pyTorch3d."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/15886.15897","article-title":"Fast phong shading","volume":"20","author":"Bishop","year":"1986","journal-title":"ACM SIGGRAPH Comput. Graph."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24568","DOI":"10.1109\/ACCESS.2017.2768078","article-title":"Robust image feature matching via progressive sparse spatial consensus","volume":"5","author":"Ma","year":"2017","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0925-7721(96)00023-5","article-title":"How good are convex hull algorithms?","volume":"7","author":"Avis","year":"1997","journal-title":"Comput. Geom."},{"key":"ref_43","unstructured":"(2020, January 11). Medetec Medical Images Medetec. Available online: http:\/\/www.medetec.co.uk\/files\/medetec-image-databases.html."},{"key":"ref_44","unstructured":"Chollet, F. (2020, May 21). Keras: Deep Learning Library for Theano and Tensorflow. Available online: https:\/\/www.datasciencecentral.com\/profiles\/blogs\/keras-deep-learning-library-for-theano-and-tensorflow."},{"key":"ref_45","first-page":"04467","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","volume":"1603","author":"Abadi","year":"2016","journal-title":"arXiv"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cmpb.2018.02.018","article-title":"Tissue classification and segmentation of pressure injuries using convolutional neural networks","volume":"159","author":"Zahia","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s10916-016-0554-x","article-title":"Spectral clustering for unsupervised segmentation of lower extremity wound beds using optical images","volume":"40","author":"Dhane","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2014.12.091","article-title":"Wound image evaluation with machine learning","volume":"164","author":"Veredas","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"315326","DOI":"10.1109\/TMI.2010.2077739","article-title":"Enhanced assessment of the wound-healing process by accurate multiview tissue classification","volume":"30","author":"Wannous","year":"2011","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2933\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:31:14Z","timestamp":1760175074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,21]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102933"],"URL":"https:\/\/doi.org\/10.3390\/s20102933","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,21]]}}}