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DSCANet: Integrating dual encoder and spatial cross-attention for polyp segmentation.

Jun Su1, Tiantian Shi1, Bogdan Adamyk2

  • 1School of Computer Science, Hubei University of Technology, Wuhan, China.

Plos One
|April 20, 2026
PubMed
Summary

This study introduces DSCANet, a novel dual-branch network for precise colorectal polyp segmentation in colonoscopies. DSCANet effectively fuses body and edge features, improving early cancer detection and diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Gastroenterology

Background:

  • Colorectal polyps detected during colonoscopy are precursors to colon cancer, necessitating accurate segmentation for early diagnosis and prevention.
  • Current polyp segmentation methods face challenges due to variations in polyp size, shape, and indistinct boundaries, impacting diagnostic reliability.
  • Automated and precise polyp segmentation is crucial for enhancing the clinical utility of colonoscopy in cancer screening.

Purpose of the Study:

  • To propose a novel deep learning network, DSCANet, for high-precision colorectal polyp segmentation.
  • To address the limitations of existing methods in segmenting polyps with diverse characteristics and unclear boundaries.
  • To improve the accuracy and efficiency of polyp detection in colonoscopic images.

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Main Methods:

  • Developed DSCANet, a dual-branch encoder-structured network integrating spatial cross-attention (SCA), bipolar fusion (BF), and flexible axis-attention (FAA) modules.
  • The network employs separate encoders for body and edge feature extraction, fused via SCA and BF modules.
  • FAA module aids the decoder in leveraging high-level semantic information for enhanced segmentation.

Main Results:

  • DSCANet demonstrated superior performance in segmenting colorectal polyps across multiple benchmark datasets.
  • The proposed network effectively fused body and edge features, leading to more accurate polyp boundary delineation.
  • Achieved high precision in segmenting polyps of varying sizes, shapes, and with poorly defined boundaries.

Conclusions:

  • DSCANet offers a robust and effective solution for automated colorectal polyp segmentation.
  • The network's architecture facilitates precise feature fusion, significantly improving segmentation accuracy in challenging medical images.
  • This advancement holds potential for enhancing colonoscopy's role in colorectal cancer prevention and early diagnosis.