Att-BrainNet: Attention-based BrainNet for lung cancer segmentation network

Most current medical image segmentation models employ a unified feature modeling strategy for all target regions. However, they overlook the significant heterogeneity in lesion structure, boundary characteristics, and semantic texture, which frequently restricts their ability to accurately segment morphologically diverse lesions in complex imaging contexts, thereby reducing segmentation accuracy and robustness. To address this issue, we propose a brain-inspired segmentation framework named BrainNet, which adopts a tri-level backbone encoder-Brain Network-decoder architecture. Such an architecture enables globally guided, locally differentiated feature modeling. We further instantiate the framework with an attention-enhanced segmentation model, termed Att-BrainNet. In this model, a Thalamus Gating Module (TGM) dynamically selects and activates structurally identical but functionally diverse Encephalic Region Networks (ERNs) to collaboratively extract lesion-specific features. In addition, an S-F image enhancement module is incorporated to improve sensitivity to boundaries and fine structures. Meanwhile, multi-head self-attention is embedded in the encoder to strengthen global semantic modeling and regional coordination. Experiments conducted on two lung cancer CT segmentation datasets and the Synapse multi-organ dataset demonstrate that Att-BrainNet outperforms existing mainstream segmentation models in terms of both accuracy and generalization. Further ablation studies and mechanism visualizations confirm the effectiveness of the BrainNet architecture and the dynamic scheduling strategy. This research provides a novel structural paradigm for medical image segmentation and holds promise for extension to other complex segmentation scenarios.