Abstract
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.
Abstract (translated)
烧伤损伤可以由热、化学和电刺激等机制引起。及时和准确的烧伤评估对于决定最终临床治疗方案至关重要。目前，烧伤评估的主要方法主要是通过视觉和触觉观察来进行，大约60%-80%的准确度较高。标准方法就是活检，另一个接近标准的方法就是像激光共聚焦成像(LDI)那样的非侵入性方法，它可以在烧伤患者远程监测时预测烧伤严重程度和所需的愈合时间，其预测准确率可以达到97%。在本文中，我们介绍了一种用于评估烧伤严重程度和分割受烧伤皮肤区域的机器学习管道。通过分割烧伤的2D彩色图像，可以清晰地区分受伤皮肤和非受伤皮肤，并明确标记局部烧伤/感兴趣区域的范围和边界，即使在烧伤患者远程监测时也是如此。我们训练了一个卷积神经网络(CNN)来分类四种烧伤严重程度。我们建立了一个注意力映射方法，称为边界注意力映射(BAM)，利用这个训练好的CNN，以准确地定位和分割烧伤区域从皮肤烧伤图像中。我们通过使用两个数据集进行了广泛的实验和评估，使用数据集1)一个包括1684个皮肤烧伤图像的更大的数据集，以及数据集2)一个包括184个皮肤烧伤图像及其相应的LDI扫描的数据集。使用第一个数据集训练的CNN，在分类四种烧伤严重程度时的平均F1得分为78%,micro/macro-平均ROC为85%。此外，比较BAM结果和LDI结果以测量烧伤边界时，我们发现我们的方法生成的分割具有91.60%的准确率，78.17%的灵敏度，和93.37%的特异性。
URL
https://arxiv.org/abs/2305.15365
