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Polyp segmentation based on implicit edge-guided cross-layer fusion networks.

Junqing Liu1,2, Weiwei Zhang3,4, Yong Liu1,2

  • 1Hubei Engineering and Technology Research Center for Construction Quality Inspection Equipment, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China.

Scientific Reports
|May 22, 2024
PubMed
Summary
This summary is machine-generated.

Accurate polyp segmentation is crucial for gastrointestinal health monitoring. The new implicit edge-guided cross-layer fusion network (IECFNet) improves polyp detection in endoscopic images, outperforming existing methods.

Keywords:
Feature fusionImplicit edgeMulti-scale feature reasoningPolyp segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Gastroenterology

Background:

  • Gastrointestinal polyps require monitoring due to potential tumor evolution.
  • Accurate polyp segmentation in endoscopic images is vital for patient management.
  • Challenges include low contrast, varied appearance, and multiple polyps.

Purpose of the Study:

  • To develop an advanced deep learning model for precise polyp segmentation.
  • To address the limitations of current polyp segmentation techniques.

Main Methods:

  • Proposed an implicit edge-guided cross-layer fusion network (IECFNet).
  • Utilized a codec pair for initial saliency map generation.
  • Employed an implicit edge-enhanced context attention module for feature aggregation.
  • Implemented a multi-scale feature reasoning module for final predictions.

Main Results:

  • IECFNet demonstrated superior performance in polyp segmentation.
  • Achieved a significant accuracy improvement of 7.9% on the ETIS dataset.
  • Outperformed conventional methods across five benchmark datasets (Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB, CVC-300).

Conclusions:

  • The proposed IECFNet effectively enhances polyp segmentation accuracy.
  • This method offers a promising solution for improved gastrointestinal polyp monitoring and diagnosis.