Integrating 3D model representation for an accurate non-invasive assessment of pressure injuries with deep learning

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dc.contributor.authorZahia, Sofia
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorElmaghraby, Adel Said
dc.date.accessioned2026-03-18T11:20:45Z
dc.date.available2026-03-18T11:20:45Z
dc.date.issued2020-05-21
dc.date.updated2026-03-18T11:20:45Z
dc.description.abstractPressure 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.en
dc.description.sponsorshipAcknowledgment to the government of the Basque Country that partially funded this project with IT905-16 grant, and the FPI DEIKER funding provided by the University of Deusto, and the ACM 2019-14 project (Aristos Campus Mundus)en
dc.identifier.citationZahia, S., Garcia-Zapirain, B., & Elmaghraby, A. (2020). Integrating 3D model representation for an accurate non-invasive assessment of pressure injuries with deep learning. Sensors (Switzerland), 20(10). https://doi.org/10.3390/S20102933
dc.identifier.doi10.3390/S20102933
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5507
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2020 by the authors
dc.subject.otherBiomedical sensing
dc.subject.otherComputer-assisted intervention
dc.subject.otherDeep learning and diagnosis
dc.subject.otherPressure injury
dc.titleIntegrating 3D model representation for an accurate non-invasive assessment of pressure injuries with deep learningen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue10
oaire.citation.titleSensors (Switzerland)
oaire.citation.volume20
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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