Abstract
Polyp segmentation from colonoscopy videos is of great importance for improving the quantitative analysis of colon cancer. However, it remains a challenging task due to (1) the large size and shape variation of polyps, (2) the low contrast between polyps and background, and (3) the inherent real-time requirement of this application, where the segmentation results should be immediately presented to the doctors during the colonoscopy procedures for their prompt decision and action. It is difficult to develop a model with powerful representation capability, yielding satisfactory segmentation results in a real-time manner. We propose a novel and efficient context-aware network, named PolypSeg, in order to comprehensively address these challenges. The proposed PolypSeg consists of two key components: adaptive scale context module (ASCM) and semantic global context module (SGCM). The ASCM aggregates the multi-scale context information and takes advantage of an improved attention mechanism to make the network focus on the target regions and hence improve the feature representation. The SGCM enriches the semantic information and excludes the background noise in the low-level features, which enhances the feature fusion between high-level and low-level features. In addition, we introduce the deep separable convolution into our PolypSeg to replace the traditional convolution operations in order to reduce parameters and computational costs to make the PolypSeg run in a real-time manner. We conducted extensive experiments on a famous public available dataset for polyp segmentation task. Experimental results demonstrate that the proposed PolypSeg achieves much better segmentation results than state-of-the-art methods with a much faster speed.
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Acknowledgement
This work was supported in part by grants from the National Natural Science Foundation of China (No. 61973221), the Natural Science Foundation of Guangdong Province, China (Nos. 2018A030313381 and 2019A1515011165), the Major Project or Key Lab of Shenzhen Research Foundation, China (Nos. JCYJ2016060 8173051207, ZDSYS201707311550233, KJYY201807031540021294 and JSGG201 805081520220065), the COVID-19 Prevention Project of Guangdong Province, China (No. 2020KZDZX1174), the Major Project of the New Generation of Artificial Intelligence (No. 2018AAA0102900) and the Hong Kong Research Grants Council (Project No. PolyU 152035/17E and 15205919).
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Zhong, J., Wang, W., Wu, H., Wen, Z., Qin, J. (2020). PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_28
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DOI: https://doi.org/10.1007/978-3-030-59725-2_28
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