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Updated: Jul 5, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

CSSNet: Cascaded spatial shift network for multi-organ segmentation.

Yeqin Shao¹, Kunyang Zhou², Lichi Zhang³

¹School of Transportation, Nantong University, Jiangsu, 226019, China.

Computers in Biology and Medicine

|January 12, 2024

Summary

This summary is machine-generated.

We introduce CSSNet, a novel network for multi-organ segmentation that overcomes limitations of CNNs and MLPs. CSSNet efficiently extracts features and refines multi-scale information for improved medical image segmentation.

Keywords:

Multi-scale feature aggregation Multilayer perceptron Organ segmentation Self-attention Transformer model

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Convolutional Neural Networks (CNNs) are widely used for organ segmentation but have limited receptive fields.
Multi-Layer Perceptron (MLP) models offer global receptive fields but are prone to overfitting on limited medical data.

Purpose of the Study:

To develop an efficient and effective deep learning model for multi-organ segmentation.
To address the limitations of existing CNN and MLP-based models in medical image analysis.

Main Methods:

Proposed a Cascaded Spatial Shift Network (CSSNet) featuring a novel cascaded spatial shift block to reduce parameters and enhance feature extraction.
Developed a feature refinement network to aggregate multi-scale features with location information.
Implemented a self-attention-based fusion strategy to focus on discriminative features.

Main Results:

CSSNet demonstrated promising multi-organ segmentation performance on the Synapse dataset.
Achieved competitive results on the LiTS dataset for liver and tumor segmentation.
Outperformed traditional CNN, MLP, and Transformer models in experimental comparisons.

Conclusions:

CSSNet offers an effective solution for multi-organ segmentation, balancing parameter efficiency and performance.
The proposed architecture successfully integrates spatial and channel attention for improved feature representation.
CSSNet shows potential for clinical applications in medical image segmentation.