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DFA-UNet: dual-stream feature-fusion attention U-Net for lymph node segmentation in lung cancer diagnosis
- Source :
- Frontiers in Neuroscience, Vol 18 (2024)
- Publication Year :
- 2024
- Publisher :
- Frontiers Media S.A., 2024.
-
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.
Details
- Language :
- English
- ISSN :
- 1662453X
- Volume :
- 18
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5630e63008d64776a10109b3142ec7e0
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fnins.2024.1448294