Development of lung segmentation method in x-ray images of children based on TransResUNet

作者：Lingdong Chen, Zhuo Yu, Jian Huang, Liqi Shu, Pekka Kuosmanen, Chen Shen, Xiaohui Ma, Jing Li, Chensheng Sun, Zheming Li, Ting Shu, Gang Yu · 发表于：Frontiers in Radiology · 年份：2023 · DOI：10.3389/fradi.2023.1190745 · 被引用次数：15 · 研究领域：COVID-19 diagnosis using AI、Medical Image Segmentation Techniques、Brain Tumor Detection and Classification

Background: Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems. Objective: In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images. Methods: The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation. Results: Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822. Conclusions: This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.