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BGF-Net: Boundary guided filter network for medical image segmentation.

Yanlin He1, Yugen Yi2, Caixia Zheng3

  • 1College of Information Sciences and Technology, Northeast Normal University, Changchun, 130117, China.

Computers in Biology and Medicine
|February 28, 2024
PubMed
Summary

This study introduces a novel boundary-guided filter network (BGF-Net) for enhanced medical image segmentation. BGF-Net effectively fuses low-level and high-level features, improving segmentation accuracy for various medical imaging tasks.

Keywords:
Boundary guided filterChannel boundary guided moduleMedical image segmentationSpatial boundary guided module

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Deep Learning

Background:

  • Accurate medical image segmentation is vital for diagnosis and treatment planning.
  • Current Convolutional Neural Network (CNN) models often fail to fully leverage high-level features to guide low-level feature extraction.
  • Attention mechanisms in existing models do not optimally utilize guided information for improved segmentation.

Purpose of the Study:

  • To develop a novel network for more accurate medical image segmentation.
  • To effectively fuse low-level and high-level features using boundary-guided information.
  • To introduce boundary guidance as a new strategy in medical image segmentation.

Main Methods:

  • Development of a boundary-guided filter network (BGF-Net).
  • Introduction of channel and spatial boundary guided modules to enhance feature extraction.
  • Proposal of a boundary guided filter to preserve structural information and guide learning.

Main Results:

  • BGF-Net demonstrated superior performance compared to state-of-the-art methods.
  • Extensive experiments on skin lesion, polyp, and gland segmentation datasets validated the network's effectiveness.
  • The proposed modules successfully guided feature extraction and improved segmentation accuracy.

Conclusions:

  • Boundary-guided information is a valuable addition to medical image segmentation.
  • BGF-Net offers a promising approach for accurate and robust medical image segmentation.
  • The novel guided modules significantly enhance the fusion of multi-level features for improved segmentation outcomes.