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Related Experiment Video

Updated: Oct 10, 2025

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management
08:50

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Published on: September 2, 2015

9.0K

A System for Wound Evaluation Support Using Depth and Image Sensors.

Ryotaro Watanabe, Keisuke Shima, Taiki Horiuchi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study introduces an automated wound assessment system using RGB-depth imaging and fully convolutional networks (FCNs). The novel technique improves wound segmentation and tissue classification accuracy compared to existing methods.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Accurate wound assessment is crucial for effective treatment and patient outcomes.
    • Current methods for wound evaluation can be subjective and time-consuming.
    • Digital imaging offers potential for objective and automated wound analysis.

    Purpose of the Study:

    • To develop an automated system for wound evaluation and treatment support.
    • To determine wound evaluation indices using RGB-depth images and fully convolutional networks (FCNs).
    • To improve the accuracy and efficiency of wound assessment compared to conventional approaches.

    Main Methods:

    • Utilized RGB-depth images for wound data acquisition.
    • Employed fully convolutional networks (FCNs) for image segmentation and analysis.

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  • Conducted segmentation experiments on wound images and surface area determination on artificial images.

    • Main Results:

    • Achieved reduced errors in wound segmentation and surface area determination.
    • Demonstrated higher levels of tissue classification accuracy (65.8%) compared to conventional methods (60.2%).
    • The proposed system requires fewer parameters than previous approaches.

    Conclusions:

    • The proposed system effectively automates wound evaluation index determination.
    • The technique shows significant improvements in accuracy and classification levels for wound assessment.
    • This approach offers a promising tool for wound treatment support systems.