Enhancing medical image segmentation via complementary CNN-transformer fusion and boundary perception

Introduction Vision Transformers (ViTs) show promise for image recognition but struggle with medical image segmentation due to a lack of inductive biases for local structures and an inability to adapt to diverse modalities like CT, endoscopy, and dermatology. Effectively combining multi-scale features from CNNs and ViTs remains a critical, unsolved challenge. Methods We propose a Pyramid Feature Fusion Network (PFF-Net) that integrates hierarchical features from pre-trained CNN and Transformer backbones. Its dual-branch architecture includes: (1) a region-aware branch for global-to-local contextual understanding via pyramid fusion, and (2) a boundary-aware branch that employs orthogonal Sobel operators and low-level features to generate precise, semantic boundaries. These boundary predictions are iteratively fed back to enhance the region branch, creating a mutually reinforcing loop between segmenting anatomical regions and delineating their boundaries. Results PFF-Net achieved state-of-the-art performance across three clinical segmentation tasks. On polyp segmentation, PFF-Net attained a Dice score of 91.87%, surpassing the TransUNet baseline (86.96%) by 5.6% and reducing the HD95 metric from 22.25 to 11.68 (a 47.5% reduction). For spleen CT segmentation, it reached a Dice score of 95.33%, outperforming ESFPNet-S (94.92%) by 4.3% while reducing the HD95 from 6.99 to 3.35 (a 52.1% reduction). In skin lesion segmentation, our model achieved a Dice score of 90.29%, which represents a 7.3% improvement over the ESFPNet-S baseline (89.64%). Discussion The results validate the effectiveness of our pyramid fusion strategy and dual-branch design in bridging the domain gap between natural and medical images. The framework demonstrates strong generalization on small-scale datasets, proving its robustness and potential for accurate segmentation across highly heterogeneous medical imaging modalities.