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Oriented-Derivative Representation for Boundary-Aware Polyp Segmentation
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AI
DOI：10.1109/TMM.2025.3599039.png)
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Abstract
En 中文
The diagnosis of colon polyps is important for the prevention of colorectal cancer. Polyp segmentation, however, is still a challenging problem given that recent medical computer-aided equipment suffers from situations of polyp variations in terms of size, color, texture, and poor illuminations in endoscopy videos. These obstacles hinder the prediction of polyp boundaries. Inspired by the observation that the values of pixels on the border region change more sharply than others, we propose the oriented-derivative (OD) representation to capture the relationship between pixels and the boundary region given distance and orientation. To adaptively use the proposed representation in arbitrary frameworks, we design plug-in modules to learn the representation and aggregate features to improve the accuracy of boundary predictions in the polyp segmentation task, which can be implemented in frameworks including the encoder-decoder and top-down architectures. Extensive experimental results show the improvement from the proposed oriented-derivative representation for the polyp segmentation task and the extendibility of our proposed modules in different architectures. Our methods achieved an improvement ranging from 0.3% to 2.5% (mDice) compared with the baseline on five publicly available datasets, including Kvasir, CVC-ClinicDB, EndoScene, CVC-ColonDB, and ETIS.
Keywords:
Endoscopy
polyp segmentation
oriented-derivative
Endoscopy
polyp segmentation
oriented-derivative
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