Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

Foltynski Piotr; Ladyzynski Piotr

首页> 外文期刊>Biocybernetics and biomedical engineering / >Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

【24h】

Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

机译：Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Uncontrolled diabetes leads to serious complications comparable to cancer. Infected foot ulcer causes a 5-year mortality of 50. Proper treatment of foot wounds is essential, and wound area monitoring plays an important role in this area. In this article, we describe an automatic wound area measurement service that facilitates area measurement and the measurement result is based on adaptive calibration for larger accuracy at curved sur-faces. Users need to take a digital picture of a wound and calibration markers and send them for analysis using an Internet page. The deep learning model based on convolutional neural networks (CNNs) was trained using 565 wound images and was used for image seg-mentation to identify the wound and calibration markers. The developed software calcu-lates the wound area based on the number of pixels in the wound region and the calibration coefficient determined from distances between ticks at calibration markers. The result of the measurement is sent back to the user at the provided e-mail address. The median relative error of wound area measurement in the wound models was 1.21. The efficacy of the CNN model was tested on 41 wounds and 73 wound models. The aver-aged values for the dice similarity coefficient, intersection over union, accuracy and speci-ficity for wound identification were 90.9, 83.9, 99.3 and 99.6, respectively. The service proved its high efficacy and can be used in wound area monitoring. The service may be used not only by health care specialists but also by patients. Thus, it is important tool for wound healing monitoring.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

著录项

来源
《Biocybernetics and biomedical engineering /》 |2023年第1期|17-29|共13页
作者
Foltynski Piotr; Ladyzynski Piotr;
展开▼
作者单位

Polish Academy of Sciences,Polish Acad Sci;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类医用一般科学;
关键词
Diabetic foot ulcer; Wound area measurement; Digital planimetry; Deep learning; Convolutional neural networks; DIABETIC FOOT SYNDROME; RISK-FACTORS;

Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks

摘要

著录项

相关主题

期刊订阅