EFCNet enhances the efficiency of segmenting clinically significant small medical objects.

Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitoring.However, existing models, including Convolutional Neural Networks (CNNs) and Transformers, struggle with these minute structures due to information loss.To address this, we introduce EFCNet, which integrates the Cross-Stage Axial Attention (CSAA) module for enhanced feature fusion and the Multi-Precision Supervision (MPS) module for improved hierarchical guidance. We evaluated EFCNet on two datasets: S-HRD, comprising 313 retinal OCT scans from patients with macular edema, and S-Polyp, a 229-image subset of the publicly available CVC-ClinicDB colonoscopy dataset. EFCNet outperformed state-of-the-art models, achieving average Dice Similarity Coefficient (DSC) gains of 4.88% on S-HRD and 3.49% on S-Polyp, alongside Intersection over Union (IoU) improvements of 3.77% and 3.25%, respectively. Notably, smaller objects benefit most, highlighting EFCNet's effectiveness where conventional models underperform. Unlike U-Net-Large, which offers marginal gains with increased scale, EFCNet's superior performance is driven by its novel design. These findings demonstrate its effectiveness and potential utility in clinical practice.