DPCrossU-Net: a dual-branch parallel CNN-Transformer network for lung nodule segmentation

Accurate segmentation of lung nodules in CT images is essential for early lung cancer screening and computer-aided diagnosis, yet remains challenging due to small target size, complex boundaries, and the limitations of existing convolutional or Transformer-based architectures in balancing local detail and global context modeling.

We propose DPCrossU-Net, a dual-branch parallel encoder-decoder network that integrates convolutional and Vision Transformer representations. The encoder employs parallel CNN and ViT branches with a Cross-Attentive Fusion (CAF) module to adaptively combine local texture and global semantic features. Multi-scale atrous convolutions are introduced at the bottleneck to enhance sensitivity to small nodules, while a dual-branch Detail Context Fusion (DCF) block in the decoder improves boundary reconstruction.

Experiments conducted on the public LIDC-IDRI dataset demonstrate that DPCrossU-Net achieves a Dice score of 85.89%, outperforming the baseline U-Net and showing superior performance, particularly in small-nodule and complex-background scenarios.

These results indicate that synergistically combining parallel CNN-Transformer feature extraction with adaptive cross-branch fusion effectively enhances lung nodule segmentation. DPCrossU-Net provides a robust and clinically applicable solution, offering improved accuracy for early lung cancer analysis and potential support for future intelligent diagnostic systems.