J Korean Neurol Assoc.  2024 Nov;42(4):322-330. 10.17340/jkna.2024.0050.

A Novel Landmark-based Semi-supervised Deep Learning Method for Cerebral Aneurysm Detection Using TOF-MRA

• Yang H
• Park J
• Kim ER
• lee M
• Rieu Z
• Kim D
• Sohn B
• lee K

• KMID: 2560987
• DOI: http://doi.org/10.17340/jkna.2024.0050

Abstract

Background
Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence.
Methods
We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network.
Results
When the number of landmarks was ten and their size was 36 mm³, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%.
Conclusions
This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Keyword

Intracranial aneurysm; Deep learning; Magnetic resonance angiography; Diagnosis; computer-assisted