IACTEC Medical Technology Group, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain.
Research Institute of Biomedical and Health Sciences (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain.
Sensors (Basel). 2021 Jan 30;21(3):934. doi: 10.3390/s21030934.
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.
热成像技术可实现对糖尿病患者足部温度的非侵入式、可及性强且易于重复的测量，有助于早期发现和定期监测，从而减少与糖尿病足疾病相关的致残情况的发生。将该应用纳入标准的糖尿病护理方案需要克服技术问题，特别是足底分割问题。在这项工作中，我们实现并评估了几种分割方法，包括传统方法和深度学习方法。为 37 名健康受试者采集了由可见光、红外和深度图像配准而成的多模态图像。所探索的分割方法既基于可见光图像，也基于红外图像，并利用深度图像提供的空间信息进行优化。此外，还从由两名独立研究人员进行的手动分割中建立了一个真实分割数据集。总的来说，所有实现方法的性能水平都令人满意。尽管在空间重叠、准确性和精度方面，基于皮肤和 U-Net 方法并利用空间信息进行优化的方法表现最佳，但 U-Net 方法的鲁棒性更优。
