A System for Wound Evaluation Support Using Depth and Image Sensors
A System for Wound Evaluation Support Using Depth and Image Sensors
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DOI：
10.1109/embc46164.2021.9629922
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Ryotaro Watanabe;Keisuke Shima;Taiki Horiuchi;Takeshi Shimizu;T. Mukaeda;K. Shimatani
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文献类型：
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作者：
Ryotaro Watanabe;Keisuke Shima;Taiki Horiuchi;Takeshi Shimizu;T. Mukaeda;K. Shimatani
本文提出了一种评估/治疗支持系统，能够自动确定rgb深度图像和全卷积网络（fcv）的伤口评估指标。基于伤口图像的分割实验和基于人工图像的表面积确定实验表明，与之前的方法相比，误差更小，参数更小/组织分类水平更高（建议：65.8%；常规：60.2%），从而证明了该技术的有效性。
This paper proposes an evaluation/treatment sup-port system enabling automatic determination of wound evaluation indices from RGB-depth images and fully convolutional networks (FCNs). Segmentation experiments based on wound images and surface area determination experiments based on artificial images showed reduced errors and smaller parameters/higher levels of tissue classification than with previous approaches (proposed: 65.8 %; conventional: 60.2 %), thereby demonstrating the effectiveness of the technique.