FTMSNet: Towards boundary-aware polyp segmentation framework based on hybrid Fourier Transform and Multi-scale Subtraction

Colorectal cancer is one of the diseases with the highest incidence and mortality rates worldwide, posing a severe threat to human life and health. Polyps are the primary cause of colorectal cancer, Colonoscopy is the gold standard for diagnosing colorectal polyps. Accurate polyp segmentation is crucial for patient diagnosis, treatment, and prognosis. However, the irregular shapes and low contrast between colorectal polyps and normal tissue make polyp segmentation a challenging task. Although deep learning-based methods have achieved promising results in this task, few approaches focus on the boundary information of colorectal polyps. In this study, to address the issue of edge blurring in polyp segmentation, we propose a boundary-aware polyp segmentation method based on a hybrid Fourier Transform and Multi-scale Convolutional neural network, referred to as FTMSNet. We introduce the Fourier Transform Module (FTM), which utilizes the Fourier Transform to retain only high-frequency information (such as boundary) in the frequency domain. By leveraging boundary information, our method achieves more precise and clearer boundary delineation for colorectal polyp segmentation. Simultaneously, the Multi-scale Feature Denoising Decoder (MFDD) we introduced is devised to mitigate noise interference during multi-scale information fusion. We validated the performance of FTMSNet on five publicly available datasets. Extensive experimental results demonstrate that our approach surpasses the state-of-the-art polyp segmentation methods.