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Accurate segmentation of polyps from colonoscopy videos is of greatsignificance to polyp treatment and early prevention of colorectal cancer.However, it is challenging due to the difficulties associated with modellinglong-range spatio-temporal relationships within a colonoscopy video. In thispaper, we address this challenging task with a novel Mixture-Attention SiameseTransformer (MAST), which explicitly models the long-range spatio-temporalrelationships with a mixture-attention mechanism for accurate polypsegmentation. Specifically, we first construct a Siamese transformerarchitecture to jointly encode paired video frames for their featurerepresentations. We then design a mixture-attention module to exploit theintra-frame and inter-frame correlations, enhancing the features with richspatio-temporal relationships. Finally, the enhanced features are fed to twoparallel decoders for predicting the segmentation maps. To the best of ourknowledge, our MAST is the first transformer model dedicated to video polypsegmentation. Extensive experiments on the large-scale SUN-SEG benchmarkdemonstrate the superior performance of MAST in comparison with thecutting-edge competitors. Our code is publicly available athttps://github.com/Junqing-Yang/MAST.

MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer