Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study.
In clinical routine, wound documentation is one of the most important contributing factors to treating patients with acute or chronic wounds. The wound documentation process is currently very time-consuming, often examiner-dependent, and therefore imprecise. This study aimed to validate a software-b...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 788 - 798 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152559515&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559515 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559515 151641929 152559515 152559515 10.1007/s10278-021-00490-x 152559515 ppf: 788 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study. aug: au: Privalov, Maxim Beisemann, Nils Barbari, Jan El Mandelka, Eric Müller, Michael Syrek, Hannah Grützner, Paul Alfred Vetter, Sven Yves affil: BG Trauma Center Ludwigshafen, Trauma and Orthopaedic Surgery, Ludwigshafen am Rhein, Germany sug: subj: Software Automation Wound Measurement Methods Photography Image Processing, Computer Assisted Neural Networks (Computer) Human Validation Studies Analysis of Variance Repeated Measures ab: In clinical routine, wound documentation is one of the most important contributing factors to treating patients with acute or chronic wounds. The wound documentation process is currently very time-consuming, often examiner-dependent, and therefore imprecise. This study aimed to validate a software-based method for automated segmentation and measurement of wounds on photographic images using the Mask R-CNN (Region-based Convolutional Neural Network). During the validation, five medical experts manually segmented an independent dataset with 35 wound photographs at two different points in time with an interval of 1 month. Simultaneously, the dataset was automatically segmented using the Mask R-CNN. Afterwards, the segmentation results were compared, and intra- and inter-rater analyses performed. In the statistical evaluation, an analysis of variance (ANOVA) was carried out and dice coefficients were calculated. The ANOVA showed no statistically significant differences throughout all raters and the network in the first segmentation round (F = 1.424 and p > 0.228) and the second segmentation round (F = 0.9969 and p > 0.411). The repeated measure analysis demonstrated no statistically significant differences in the segmentation quality of the medical experts over time (F = 6.05 and p > 0.09). However, a certain intra-rater variability was apparent, whereas the Mask R-CNN consistently provided identical segmentations regardless of the point in time. Using the software-based method for segmentation and measurement of wounds on photographs can accelerate the documentation process and improve the consistency of measured values while maintaining quality and precision. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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