Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Fetal brain MRI is crucial for diagnosing abnormalities but is significantly hampered by fetal motion, leading to image artifacts.
• Manual assessment of fetal MR image quality is time-consuming and subjective.

Purpose of the Study:

• To develop a semi-supervised deep learning method for automated detection of motion-corrupted slices in fetal brain MRI.
• To improve the efficiency and accuracy of fetal MR image quality assessment.

Main Methods:

• A semi-supervised deep learning approach utilizing a mean teacher model with ROI consistency loss.
• The model enforces consistency between student and teacher networks, focusing on the brain region to detect artifacts.

Main Results:

• The method achieved approximately 6% higher accuracy compared to supervised learning methods.
• It outperformed existing state-of-the-art semi-supervised learning techniques on a large dataset.
• Online evaluation on an MR scanner demonstrated feasibility for real-time quality assessment.

Conclusions:

• The proposed semi-supervised deep learning method effectively detects motion artifacts in fetal brain MRI.
• This approach enhances diagnostic accuracy and offers a feasible solution for online image quality control during scans.