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Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation.

Guangcan Qu¹, Beichen Lu¹, Jialin Shi¹

¹School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.

Physics in Medicine and Biology

|February 26, 2024

Summary

This study introduces a novel semi-supervised learning method for brain tumor segmentation using motion artifact augmentation. The proposed approach, MAPSS, enhances accuracy with limited labeled data and improves robustness against image artifacts.

Keywords:

brain tumor medical image segmentation robustness semi-supervised learning

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neuro-oncology Imaging

Background:

Accurate MRI image segmentation is crucial for brain tumor diagnosis and treatment planning.
Fully supervised methods face limitations due to high annotation costs and limited datasets.
Image quality issues like noise and motion artifacts hinder segmentation performance.

Purpose of the Study:

To develop an automated, accurate, and robust brain tumor segmentation method.
To address the challenges of limited labeled data and motion artifacts in medical image segmentation.
To reduce the workload of clinical doctors in brain tumor diagnosis.

Main Methods:

Proposed MAPSS (Motion-Artifact-Augmented Pseudo-Label network) for semi-supervised segmentation.
Combined motion artifact data augmentation with a pseudo-label training framework.
Conducted experiments on the BraTS2020 dataset for brain tumor segmentation.

Main Results:

MAPSS achieved accurate brain tumor segmentation with minimal labeled data.
The method demonstrated robustness against motion artifacts in MRI images.
Evaluated generalization performance on the Left Atrium dataset.

Conclusions:

MAPSS offers a significant advancement in semi-supervised brain tumor segmentation.
The approach effectively handles limited data and image artifacts, improving clinical utility.
This method holds great potential for assisting in treatment planning and enhancing patient care quality.