Center for Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India.
Center for Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India; Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India.
Burns. 2021 Jun;47(4):854-862. doi: 10.1016/j.burns.2020.08.016. Epub 2020 Sep 12.
Burn injuries are one of the most severe forms of wounds and trauma across the globe. Automated burn diagnosis methods are needed to provide timely treatment to the concerned patients. Artificial intelligence is playing a vital role in developing automated tools and techniques for medical problems. However, the use of advanced AI techniques for color images based burn region segmentation is not much explored.
In this work, we explore the use of deep learning for the challenging problem of burn region segmentation. We prepared a pixel-wise labelled new burn images dataset for segmentation and investigated the efficacy of existing state-of-the-art color images based semantic image segmentation techniques. Lately, we proposed a new convolution neural network (CNN) that uses atrous convolution for encoding rich contextual information and utilizes pre-trained model ResNet-101 for better extraction of low-level and middle-level layer features.
The proposed approach achieves the state-of-the-art performance on the prepared burn image dataset with 77.6% of Mathews correlation coefficient (MCC) and 93.4% of accuracy. The improvement of 11.6/5.8/6.9/1.2% is observed in precision, Dice similarity coefficient, Jaccard index and specificity, in comparison to the second best performance.
In this work, we propose a CNN based novel method for performing burn-region segmentation in color images and evaluate it using newly prepared Burn Images dataset. The experimental results illustrate its effectiveness in comparison to existing approaches. Further, the proposed pixel-level segmentation method could be useful in estimating the burn surface area and burn severity in an accurate and time efficient manner.
烧伤是全球最严重的创伤之一。需要自动化的烧伤诊断方法来为相关患者提供及时的治疗。人工智能在开发用于医疗问题的自动化工具和技术方面发挥着至关重要的作用。然而，基于颜色图像的高级 AI 技术在烧伤区域分割中的应用尚未得到充分探索。
在这项工作中，我们探索了深度学习在烧伤区域分割这一具有挑战性问题中的应用。我们准备了一个像素级标记的新烧伤图像数据集用于分割，并研究了现有的基于颜色图像的语义图像分割技术的效果。最近，我们提出了一种新的卷积神经网络（CNN），该网络使用空洞卷积来编码丰富的上下文信息，并利用预训练的 ResNet-101 模型来更好地提取低层次和中间层次的特征。
所提出的方法在准备好的烧伤图像数据集上取得了最先进的性能，马修斯相关系数（MCC）为 77.6%，准确率为 93.4%。与第二好的性能相比，精度、Dice 相似系数、Jaccard 指数和特异性分别提高了 11.6/5.8/6.9/1.2%。
在这项工作中，我们提出了一种基于 CNN 的新方法，用于对彩色图像进行烧伤区域分割，并使用新准备的烧伤图像数据集对其进行评估。实验结果表明，与现有方法相比，该方法具有有效性。此外，所提出的像素级分割方法可以在准确和高效的方式中用于估计烧伤面积和烧伤严重程度。
