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2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System.

Damir Filko1, Emmanuel Karlo Nyarko1

  • 1Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, HR-31000 Osijek, Croatia.

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Summary

This study presents an automated system for chronic wound assessment using a robot arm, RGB-D camera, and 3D scanner. It accurately segments wounds, providing key geometric parameters to improve healing outcomes.

Keywords:
2D3Dactive contour modelchronic woundconvolutional neural networkmeasurementrobotsegmentation

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Wound Care Technology

Background:

  • Chronic wounds pose a significant global health and economic burden, exacerbated by rising rates of diabetes, obesity, and aging populations.
  • Accurate and rapid wound assessment is crucial for effective management, complication reduction, and accelerated healing.
  • Current assessment methods can be time-consuming and subjective, necessitating advanced technological solutions.

Purpose of the Study:

  • To develop and evaluate an automated wound segmentation system for precise chronic wound assessment.
  • To integrate 2D and 3D imaging techniques for comprehensive wound surface reconstruction.
  • To extract key geometric parameters (perimeter, area, volume) for quantitative wound analysis.

Main Methods:

  • A novel wound recording system incorporating a 7-Degrees of Freedom (7-DoF) robot arm equipped with an RGB-D camera and a high-precision 3D scanner.
  • A hybrid segmentation approach combining MobileNetV2 for 2D image classification and an active contour model operating on a 3D mesh for refined contour detection.
  • Generation of a 3D wound model excluding surrounding healthy skin.

Main Results:

  • Successful development of an automated system for segmenting chronic wounds from surrounding tissue.
  • Accurate extraction of 3D wound models and precise measurement of geometric parameters including perimeter, area, and volume.
  • Demonstration of a combined 2D and 3D segmentation technique for enhanced wound characterization.

Conclusions:

  • The developed automated system offers a fast and accurate method for chronic wound assessment, improving upon traditional techniques.
  • Integration of robotic imaging and advanced segmentation algorithms provides a robust platform for quantitative wound analysis.
  • This technology has the potential to significantly aid in clinical decision-making and improve patient outcomes in chronic wound management.