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Updated: Jul 21, 2025
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
Published on: June 21, 2024
Development of lung segmentation method in x-ray images of children based on TransResUNet
Lingdong Chen1,2,3, Zhuo Yu4, Jian Huang1,2,3
1Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.
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.
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