Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144.

中文翻译：

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使用深度学习进行自动烧伤图像分割和烧伤深度诊断的框架

烧伤是一种常见的创伤性疾病，发病率和死亡率都很高。烧伤的治疗需要对烧伤伤口和烧伤深度进行准确可靠的诊断，这在某些情况下可以挽救生命。然而，由于烧伤创面的复杂性，烧伤的早期诊断缺乏准确性和差异性。因此，我们使用深度学习技术使烧伤诊断自动化和标准化，以减少人为错误并改进烧伤诊断。首先，创建带有详细烧伤区域分割和烧伤深度标记的烧伤数据集。然后，提出了一种基于深度学习方法的端到端框架，用于高级烧伤区域分割和烧伤深度诊断。该框架首先用于对烧伤图像中的烧伤区域进行分割。在此基础上，通过扩展网络输出结构和烧伤数据集的标签，可以实现烧伤面积占全身表面积（TBSA）百分比的计算。然后，该框架用于分割多个烧伤深度区域。最后，网络在烧伤和无烧伤区域的分割方面取得了最好的结果，IOU为0.8467。而对于多个烧伤深度区域分割，最佳平均IOU为0.5144。