Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close second would be non-invasive methods like Laser Doppler Imaging (LDI) assessments, which have up to 97% accuracy in predicting burn severity and the required healing time. In this paper, we introduce a machine learning pipeline for assessing burn severities and segmenting the regions of skin that are affected by burn. Segmenting 2D colour images of burns allows for the injured versus non-injured skin to be delineated, clearly marking the extent and boundaries of the localized burn/region-of-interest, even during remote monitoring of a burn patient. We trained a convolutional neural network (CNN) to classify four severities of burns. We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images. We demonstrated the effectiveness of our proposed pipeline through extensive experiments and evaluations using two datasets; 1) A larger skin burn image dataset consisting of 1684 skin burn images of four burn severities, 2) An LDI dataset that consists of a total of 184 skin burn images with their associated LDI scans. The CNN trained using the first dataset achieved an average F1-Score of 78% and micro/macro- average ROC of 85% in classifying the four burn severities. Moreover, a comparison between the BAM results and LDI results for measuring injury boundary showed that the segmentations generated by our method achieved 91.60% accuracy, 78.17% sensitivity, and 93.37% specificity.

翻译：烧伤可由热力、化学及电击等机制造成。快速准确的烧伤评估对确定临床治疗方案至关重要。目前，通过视诊与触诊进行烧伤评估的主要方法准确率约为60%-80%。金标准是活组织检查，而激光多普勒成像（LDI）等无创方法可视为次要标准，其在预测烧伤严重程度及所需愈合时间方面准确率高达97%。本文提出一种用于评估烧伤严重程度并分割受累皮肤区域的机器学习流程。通过分割烧伤区域的二维彩色图像，可明确区分受损与未受损皮肤，清晰标示局部烧伤/感兴趣区域的边界与范围，甚至在远程监测烧伤患者时亦能实现。我们训练了一个卷积神经网络（CNN）对四种烧伤严重程度进行分类，并构建了边界注意力映射（BAM）这一显著图生成方法，利用该训练后的CNN从皮肤烧伤图像中精准定位并分割烧伤区域。通过两个数据集的广泛实验与评估验证了所提流程的有效性：1）包含1684张四种烧伤严重程度图像的较大规模皮肤烧伤图像数据集；2）包含184张皮肤烧伤图像及其对应LDI扫描的LDI数据集。使用第一个数据集训练的CNN在四类烧伤严重程度分类中平均F1分数达78%，微平均/宏平均ROC达85%。此外，将BAM结果与LDI结果进行损伤边界测量对比显示，本方法生成的分割区域准确率达91.60%，敏感度78.17%，特异度93.37%。