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Related Experiment Video

Updated: Jun 18, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

BMSNet: A Boundary-Guided Multi-Scale Polyp Segmentation Network.

Tianying Gao1, Zihao Song1, Zhenjian Yang1

  • 1Tianjin Chengjian University, School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin, 300384, China.

Biomedical Physics & Engineering Express
|June 16, 2026
PubMed
Summary

BMSNet improves polyp segmentation in colonoscopy images using a novel boundary-guided multi-scale network. This method enhances computer-aided diagnosis for early colorectal cancer screening by accurately identifying polyps.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate polyp segmentation is crucial for computer-aided diagnosis and early colorectal cancer screening.
  • Challenges include variations in polyp size, shape, texture, blurred boundaries, and low contrast.

Purpose of the Study:

  • To propose BMSNet, a boundary-guided multi-scale network for accurate polyp segmentation.
  • To address the limitations of existing methods in handling polyp variations and boundary ambiguity.

Main Methods:

  • Utilized an SMT-T backbone for efficient multi-level feature extraction.
  • Introduced a Boundary Prediction Module with bidirectional feature interaction and differentiable Canny-based supervision.
  • Designed a Boundary-Guided Feature Enhancement Module incorporating frequency-domain enhancement, multi-scale feature interaction, and boundary-guided modulation.
Keywords:
Edge attentionFeature fusionPolyp segmentation

Related Experiment Videos

Last Updated: Jun 18, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Employed a Feature Fusion Unit for progressive integration of multi-scale features.

    • Main Results:

    • BMSNet achieved high Dice scores on benchmark datasets: 0.943 (ClinicDB), 0.920 (Kvasir), 0.828 (ColonDB), 0.825 (ETIS), and 0.913 (CVC-300).
    • Demonstrated effectiveness across diverse datasets, indicating robust performance.
    • Showcased a favorable balance between segmentation accuracy and computational efficiency.

    Conclusions:

    • BMSNet offers a promising solution for accurate polyp segmentation in colonoscopy images.
    • The boundary-guided approach effectively enhances feature representation and segmentation accuracy.
    • The method holds potential for improving computer-aided diagnosis and colorectal cancer screening.