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Related Experiment Video

Updated: Dec 24, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network.

Hojin Kim¹, Jinhong Jung¹, Jieun Kim²

¹Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.

Scientific Reports

|April 12, 2020

Summary

This summary is machine-generated.

This study introduces an automated system for segmenting abdominal organs in CT scans using a 3D U-Net convolutional neural network. The automated method significantly reduces segmentation time while maintaining high accuracy for radiotherapy planning.

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Area of Science:

Medical Imaging
Radiotherapy
Artificial Intelligence

Background:

Organ segmentation in radiotherapy is crucial but time-consuming.
Convolutional Neural Networks (CNNs) enable automated segmentation.
Accurate auto-segmentation improves treatment planning efficiency.

Purpose of the Study:

To develop and evaluate a 3D U-Net based auto-segmentation framework for abdominal organs.
To compare the accuracy and efficiency of the proposed method against atlas-based segmentation and inter-observer variability.
To assess the clinical utility of automated organ segmentation in radiotherapy.

Main Methods:

Utilized a 3D-patch-based U-Net convolutional neural network with graph-cut post-processing.
Input: 3D CT image patches (64x64x64 voxels) for liver, stomach, duodenum, and kidneys.
Trained and tested on 120 CT simulation scans (80 training, 20 validation, 20 testing).

Main Results:

U-Net auto-segmentation outperformed atlas-based methods across all abdominal organs.
Achieved comparable accuracy to inter-observer segmentation, particularly for the liver and kidneys.
Reduced average segmentation time from 22.6 minutes (manual) to 7.1 minutes (automated).

Conclusions:

The 3D U-Net framework offers a clinically valuable solution for abdominal organ auto-segmentation.
Demonstrates significant improvements in both accuracy and time-efficiency for radiotherapy planning.
Potential to streamline radiotherapy workflows and enhance treatment precision.