Dr. Min Zhang Profile

Dr. Min Zhang

at Zhejiang Univ

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Proceedings Article | 7 April 2023 Paper

Gate-SBNet: gate semantic boundary network for colorectal polyp segmentation

Jun Wang, Jihong Sun, Xiaoyin Xu, Min Zhang

Proceedings Volume 12465, 124651K (2023) https://doi.org/10.1117/12.2653779

KEYWORDS: Semantics, Polyps, Image segmentation, Performance modeling, Convolution, Data modeling, Colorectal cancer, Error control coding, Data processing, Network architectures

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Accurate polyp segmentation from colonoscopy is essential for the early detection of colorectal cancer. However, the variety of polyps manifested in images and the blurry boundary between a polyp and its surrounding mucosa make segmentation challenging. Hence, it is crucial to accurately identify regional boundaries of polyps in colonoscopy. In this paper, we propose Gated Semantic Boundary Network (Gate-SBNet), which is a novel twostream CNN architecture based on an encoder-decoder framework to segment polyps in colonoscopy images. One stream of Gate-SBNet uses a pre-trained ConvNeXt-B model from the image classification task as the semantic encoder to obtain multi-level semantic features from colonoscopy images. Another branch uses the Semantic Boundary Learning Module (SBLM) to learn boundary features based on multi-level semantic features, which process information in parallel with the semantic encoder. By introducing the Gate Convolution Layer (GCLs) into the SBLM module, the semantic information is converted more accurately to boundary information. Therefore, only boundary-related information will be processed by the SBLM. Then, we merge semantic and boundary features as input to an Unet model to obtain the final segmentation result. Our proposed approach was evaluated on five benchmark datasets: Kvasir, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-LaribPolypDB. Experiments have demonstrated that it is an efficient architecture and capable of making accurate predictions about object boundaries and significantly improving the performance of finding thin and small objects.

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