用于远程医疗援助的压疮护理系统：基于注意力模型的残余U-网伤口区域分割
A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation
摘要：
越来越多的残疾患者或行动不便的老年人经常患有压疮。受影响的地区需要定期检查，但他们很难进入医院。一些远程诊断系统正被用于他们，但在定期检查病人的状态有局限性。在本文中，我们提出了一个远程医疗助理，可以帮助压疮管理与图像处理技术。所提出的系统包括具有用于伤口分割和分析的深度学习模型的移动应用程序。由于没有足够的数据来训练深度学习模型，我们使用来自相关领域的预训练模型和适合此任务的数据增强。首先，使用双线性插值的图像预处理方法来调整图像的大小和归一化图像。其次，对于数据增强，我们使用旋转，反射和分水岭算法。第三，我们使用一个预训练的深度学习模型，该模型是从类似于压疮图像的皮肤伤口图像生成的。最后，我们添加了一个注意力模块，可以提供有关压疮图像特征的提示。由此产生的模型提供了99.0%的准确性，99.99%的交集（IoU），和骰子相似系数（DSC）为93.4%的压疮分割，这是优于现有的结果。
英文摘要：
Increasing numbers of patients with disabilities or elderly people with mobility issues often suffer from a pressure ulcer. The affected areas need regular checks, but they have a difficulty in accessing a hospital. Some remote diagnosis systems are being used for them, but there are limitations in checking a patient's status regularly. In this paper, we present a remote medical assistant that can help pressure ulcer management with image processing techniques. The proposed system includes a mobile application with a deep learning model for wound segmentation and analysis. As there are not enough data to train the deep learning model, we make use of a pretrained model from a relevant domain and data augmentation that is appropriate for this task. First of all, an image preprocessing method using bilinear interpolation is used to resize images and normalize the images. Second, for data augmentation, we use rotation, reflection, and a watershed algorithm. Third, we use a pretrained deep learning model generated from skin wound images similar to pressure ulcer images. Finally, we added an attention module that can provide hints on the pressure ulcer image features. The resulting model provides an accuracy of 99.0%, an intersection over union (IoU) of 99.99%, and a dice similarity coefficient (DSC) of 93.4% for pressure ulcer segmentation, which is better than existing results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
