Development of lung segmentation method in x-ray images of children based on TransResUNet.
Front Radiol
; 3: 1190745, 2023.
Article
en En
| MEDLINE
| ID: mdl-37492393
ABSTRACT
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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Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Idioma:
En
Revista:
Front Radiol
Año:
2023
Tipo del documento:
Article