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Given the close association between colorectal cancer and polyps, thediagnosis and identification of colorectal polyps play a critical role in thedetection and surgical intervention of colorectal cancer. In this context, theautomatic detection and segmentation of polyps from various colonoscopy imageshas emerged as a significant problem that has attracted broad attention.Current polyp segmentation techniques face several challenges: firstly, polypsvary in size, texture, color, and pattern; secondly, the boundaries betweenpolyps and mucosa are usually blurred, existing studies have focused onlearning the local features of polyps while ignoring the long-rangedependencies of the features, and also ignoring the local context and globalcontextual information of the combined features. To address these challenges,we propose FLDNet (Foreground-Long-Distance Network), a Transformer-basedneural network that captures long-distance dependencies for accurate polypsegmentation. Specifically, the proposed model consists of three main modules:a pyramid-based Transformer encoder, a local context module, and aforeground-Aware module. Multilevel features with long-distance dependencyinformation are first captured by the pyramid-based transformer encoder. On thehigh-level features, the local context module obtains the local characteristicsrelated to the polyps by constructing different local context information. Thecoarse map obtained by decoding the reconstructed highest-level features guidesthe feature fusion process in the foreground-Aware module of the high-levelfeatures to achieve foreground enhancement of the polyps. Our proposed method,FLDNet, was evaluated using seven metrics on common datasets and demonstratedsuperiority over state-of-the-art methods on widely-used evaluation measures.

FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies