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Updated: Jun 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Medical image segmentation network based on multi-scale frequency domain filter.

Yufeng Chen¹, Xiaoqian Zhang¹, Lifan Peng¹

¹School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 5, 2024

Summary

This summary is machine-generated.

This study introduces FDFUNet, a novel deep learning model for medical image segmentation. It enhances segmentation performance and generalization by addressing limitations in existing UNet models using new convolutional blocks and frequency domain analysis.

Keywords:

Channel Frequency domain Medical image segmentation Spatial domain UNet

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Area of Science:

Deep learning
Medical image analysis
Computer-aided diagnosis

Background:

UNet and its variants are powerful for medical image segmentation but have limitations.
Existing methods struggle with insufficient receptive fields, channel feature redundancy, and inter-channel relationships.
These limitations result in suboptimal segmentation performance and poor generalization.

Purpose of the Study:

To develop an improved deep learning framework for medical image segmentation.
To enhance the segmentation performance and generalization ability of UNet-based models.
To introduce novel modules addressing limitations in receptive field, depth, and feature channel interactions.

Main Methods:

Proposed the Double residual depthwise atrous convolution (DRDAC) block to improve receptive field and depth.
Introduced the Multi-scale frequency domain filter (MFDF) module to capture global information in the frequency domain.
Redesigned Axial selection channel attention (ASCA) to model interrelationships among feature channels.
Developed the FDFUNet baseline method integrating these novel modules.

Main Results:

Extensive experiments were conducted on five public medical image datasets.
The proposed FDFUNet demonstrated superior segmentation performance compared to state-of-the-art methods.
The method showed enhanced generalization ability across different datasets.

Conclusions:

The FDFUNet model effectively addresses limitations of traditional UNet architectures in medical image segmentation.
The integration of DRDAC, MFDF, and ASCA modules significantly improves segmentation accuracy and robustness.
The proposed frequency domain approach offers a promising direction for advanced medical image analysis.