PolypSeg+: A Lightweight Context-Aware Network for Real-Time Polyp Segmentation

Huisi Wu, Zebin Zhao, Jiafu Zhong, Wei Wang, Zhenkun Wen, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Automatic polyp segmentation from colonoscopy videos is a prerequisite for the development of a computer-assisted colon cancer examination and diagnosis system. However, it remains a very challenging task owing to the large variation of polyps, the low contrast between polyps and background, and the blurring boundaries of polyps. More importantly, real-time performance is a necessity of this task, as it is anticipated that the segmented results can be immediately presented to the doctor during the colonoscopy intervention for his/her prompt decision and action. It is difficult to develop a model with powerful representation capability, yielding satisfactory segmentation results and, simultaneously, maintaining real-time performance. In this article, we present a novel lightweight context-aware network, namely, PolypSeg+, attempting to capture distinguishable features of polyps without increasing network complexity and sacrificing time performance. To achieve this, a set of novel lightweight techniques is developed and integrated into the proposed PolypSeg+, including an adaptive scale context (ASC) module equipped with a lightweight attention mechanism to tackle the large-scale variation of polyps, an efficient global context (EGC) module to promote the fusion of low-level and high-level features by excluding background noise and preserving boundary details, and a lightweight feature pyramid fusion (FPF) module to further refine the features extracted from the ASC and EGC. We extensively evaluate the proposed PolypSeg+ on two famous public available datasets for the polyp segmentation task: 1) Kvasir-SEG and 2) CVC-Endoscenestill. The experimental results demonstrate that our PolypSeg+ consistently outperforms other state-of-the-art networks by achieving better segmentation accuracy in much less running time. The code is available at https://github.com/szu-zzb/polypsegplus.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusAccepted/In press - 2022


  • Cancer
  • Colonoscopy
  • context-aware network
  • Data mining
  • Feature extraction
  • feature pyramid fusion (FPF)
  • Image segmentation
  • lightweight deep learning model
  • real-time polyp segmentation
  • Real-time systems
  • Task analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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