WDRE-NET: Wavelet-Differential Convolution and Region-Expansion to Enhance Weakly Supervised Adjacent Nuclei Segmentation

Nuclei segmentation is crucial in medical image analysis. Weakly supervised methods based on point annotations alleviate the labor-intensive process of pixel-level labeling. However, existing approaches struggle with closely adjacent nuclei, leading to imprecise supervision signals, prediction noise, and reduced accuracy. To address these issues, we propose WDRE-NET, a novel end-to-end weakly supervised framework. Our framework incorporates a Wavelet-based Differential Convolutional Module (WDCM) to enhance the model’s ability to capture nuclei boundaries through multi-scale, multi-directional feature extraction, improving the differentiation of adjacent nuclei. Additionally, a Region-Expansion mechanism (RE) iteratively extends point annotations to cover more high-confidence areas, generating reliable supervision signals. This reduces prediction noise and missed detections, improving segmentation performance. Extensive experiments on three public datasets show that WDRE-NET outperforms state-of-the-art methods while effectively addressing the challenge of adjacent nuclei.