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Related Experiment Video

Updated: Jul 9, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Dual-branch multi-information aggregation network with transformer and convolution for polyp segmentation.

Wenyu Zhang¹, Fuxiang Lu¹, Hongjing Su¹

¹School of Information Science and Engineering, Lanzhou University, China.

Computers in Biology and Medicine

|December 8, 2023

Summary

This summary is machine-generated.

This study introduces DBMIA-Net, a deep learning model for accurate colorectal polyp segmentation in colonoscopy images. The novel network effectively handles challenging cases, improving diagnostic accuracy for computer-aided diagnosis (CAD).

Keywords:

CNN Information aggregation Polyp segmentation Transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Computer-Aided Diagnosis (CAD) systems are crucial for polyp detection.
Deep learning enhances polyp segmentation accuracy, surpassing human experts.
Challenges remain in segmenting polyps affected by motion blur, light reflection, and diverse visual characteristics.

Purpose of the Study:

To propose a novel dual-branch multi-information aggregation network (DBMIA-Net) for accurate and efficient colorectal polyp segmentation.
To address limitations of existing methods in handling noisy and varied polyp appearances.

Main Methods:

A dual-branch encoder utilizing transformer and convolutional neural networks (CNNs) for feature extraction.
Multi-information aggregation modules (GIA and EIA) in the decoder for adaptive multi-scale feature fusion.
An adaptive channel graph convolution (ACGC) to improve channel feature association.

Main Results:

DBMIA-Net demonstrated superior segmentation performance across five public datasets and six evaluation metrics compared to state-of-the-art methods.
Achieved a 94.12% mean Dice score on the CVC-ClinicDB dataset, a 4.22% improvement over PraNet.
Exhibited enhanced fitting and generalization abilities.

Conclusions:

DBMIA-Net offers a significant advancement in colorectal polyp segmentation for CAD systems.
The proposed network effectively segments diverse polyps, even in challenging imaging conditions.
DBMIA-Net shows strong potential for improving colonoscopy analysis and patient outcomes.