Weakly-Supervised Nuclei Segmentation Integrating Hybrid Decoder and Graph-Based Spatial Modeling

Nuclei segmentation is crucial in medical image analysis, but accurately segmenting complex nuclei shapes under weak supervision with only point annotations remains a significant challenge. To address this, we propose an innovative weakly supervised segmentation method, which includes a novel Hybrid Decoder with two skip connections, a Dual-Gated Attention module, and Laplacian operators. These components collaborate to enhance fine-grained feature representation and refine boundary detection. Additionally, we employ a Graph Convolutional Network (GCN) to model spatial dependencies between pixels via a graph structure, integrating the GCN-generated labels into the loss function to further boost segmentation accuracy and optimize nuclei boundaries and structures. Experimental results demonstrate that our method outperforms existing approaches, particularly in challenging tasks with complex nuclei.