A Novel Landmark-based Semi-supervised Deep Learning Method for Cerebral Aneurysm Detection Using TOF-MRA
- Author:
Hyeonsik YANG
1
;
Jieun PARK
;
Eunyoung Regina KIM
;
Minho LEE
;
ZunHyan RIEU
;
Donghyeon KIM
;
Beomseok SOHN
;
Kijeong LEE
Author Information
- Publication Type:Original Article
- From:Journal of the Korean Neurological Association 2024;42(4):322-330
- CountryRepublic of Korea
- Language:Korean
-
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.
