Shape-aware medical image segmentation via frequency domain partitioning

Precise medical image segmentation is vital for computer-aided diagnosis, yet current methods struggle with subtle endoscopic areas where lesions and normal tissue appear similar. To address this, we propose a shape-aware partitioning model with a dual-branch architecture. Its high-frequency branch captures edges and fine details, while the low-frequency branch focuses on overall shape and color distribution. The proposed model integrates these features via a hybrid decoder and a chimeric wavelet block, facilitating continuous bilateral information interaction. We also introduce a dual-domain loss function to comprehensively evaluate model output against ground truth, especially when pixel value differences are small but frequency domain differences are significant. The proposed method markedly enhances the accuracy and efficiency of computer-aided diagnosis, particularly in polyp and skin lesion segmentation. By accurately capturing lesion shape and volume, it provides a robust tool crucial for disease grading and treatment planning. Moreover, it outperforms comparable hybrid architectures integrating convolutional neural networks and transformers on public endoscopic polyp segmentation benchmarks. Quantitatively, it achieves a 15.29% higher intersection over union than conventional hybrid networks with a 33.75 giga floating-point operations reduction. Furthermore, it shows a 5.93% improvement over hierarchical hybrid models with an 11.74 giga floating-point operations decrease.