DFA-UNet: dual-stream feature-fusion attention U-Net for lymph node segmentation in lung cancer diagnosis.
Front Neurosci
; 18: 1448294, 2024.
Article
en En
| MEDLINE
| ID: mdl-39077427
ABSTRACT
In bronchial ultrasound elastography, accurately segmenting mediastinal lymph nodes is of great significance for diagnosing whether lung cancer has metastasized. However, due to the ill-defined margin of ultrasound images and the complexity of lymph node structure, accurate segmentation of fine contours is still challenging. Therefore, we propose a dual-stream feature-fusion attention U-Net (DFA-UNet). Firstly, a dual-stream encoder (DSE) is designed by combining ConvNext with a lightweight vision transformer (ViT) to extract the local information and global information of images; Secondly, we propose a hybrid attention module (HAM) at the bottleneck, which incorporates spatial and channel attention to optimize the features transmission process by optimizing high-dimensional features at the bottom of the network. Finally, the feature-enhanced residual decoder (FRD) is developed to improve the fusion of features obtained from the encoder and decoder, ensuring a more comprehensive integration. Extensive experiments on the ultrasound elasticity image dataset show the superiority of our DFA-UNet over 9 state-of-the-art image segmentation models. Additionally, visual analysis, ablation studies, and generalization assessments highlight the significant enhancement effects of DFA-UNet. Comprehensive experiments confirm the excellent segmentation effectiveness of the DFA-UNet combined attention mechanism for ultrasound images, underscoring its important significance for future research on medical images.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Front Neurosci
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza