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Updated: Apr 8, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Segmenting multi-modal abdominal multi-organ using the CADSTransN-Net model.

Peng Sun^1,2, Guichong Wu¹, Yutao Tan¹

¹School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, China.

Journal of X-Ray Science and Technology

|April 7, 2026

Summary

This summary is machine-generated.

This study introduces CADSTransN-Net, a novel deep learning model that significantly improves abdominal organ segmentation accuracy using the diverse AMOS22 dataset. The model demonstrates robust performance for multi-modal abdominal multi-organ segmentation tasks.

Keywords:

AMOS22 CADSTransN-Net model CT MRI abdominal image segmentation

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Last Updated: Apr 8, 2026

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Area of Science:

Medical Imaging Analysis
Deep Learning for Segmentation
Computational Anatomy

Background:

Deep learning segmentation performance is limited by dataset diversity.
The AMOS22 dataset offers large-scale, varied clinical data to improve algorithm robustness.
Enhancing abdominal organ segmentation is crucial for clinical applications.

Purpose of the Study:

To develop and validate CADSTransN-Net for abdominal organ segmentation within the AMOS22 challenge.
To optimize segmentation accuracy and robustness using novel architectural components.
To address limitations in current deep learning models for medical image segmentation.

Main Methods:

Developed CADSTransN-Net featuring an N-shaped feature flow path for efficient encoder-decoder fusion.
Integrated a convolutional attention mechanism to prioritize anatomically relevant regions.
Employed layer-wise deep supervision for improved gradient propagation and faster convergence.

Main Results:

Achieved a Dice Similarity Coefficient (DSC) of 0.907 and Normalized Surface Dice (NSD) of 0.850 on the AMOS22 dataset.
Demonstrated high accuracy in boundary consistency with HD(95%) of 3.98 mm and ASD of 0.75 mm.
Showcased precise volume estimation with AVD of 39,755.88 mm³ and RVD of 1.53%.

Conclusions:

CADSTransN-Net effectively addresses the challenges of the AMOS22 dataset, providing robust multi-modal abdominal multi-organ segmentation.
The model exhibits excellent performance across region overlap, boundary definition, and volume accuracy metrics.
Offers a reliable solution with significant potential for clinical applications like surgical navigation.