Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging

Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) can effectively reduce the risk of rupture. Despite the importance of early detection, manual image screening remains a laborious and inefficient process. The current thrust of research in computer-aided detection (CAD) methods is to refine neural networks to improve diagnostic accuracy. In our preliminary work, we discovered that utilizing arterial contour as external knowledge guidance can substantially enhance the detection capabilities of existing networks, thus providing a new perspective for optimizing aneurysm detection techniques.

In this paper, we introduce an innovative approach to building a cerebral aneurysm detection model that employs artery fragment as external guidance data. We propose a hypothesis regarding the optimal distribution pattern of knowledge-guided data based on the brain artery volume of interest (VOI), and based on this, we have developed an end-to-end fully automatic and data-adaptive artery fragment generation method tailored for both training and testing data. Utilizing a multicenter dataset, we tested the performance enhancement capabilities of this method for two commonly used vascular networks, SE-3D UNet and VNet. Furthermore, we conducted a comparative analysis with other guidance methods using the best-performing model to elucidate the mechanisms behind the improved guidance efficacy of our approach.

This study amassed a total of 500 cases of 3.0T TOF-MRA data from 13 devices across 6 hospitals, with 400 cases designated as the training set and 100 cases as the test set, while data from one device was exclusively used for testing. The proposed method showed significant improvements for both SE-3D UNet and VNet. Specifically, SE 3D UNET saw a 13.89 % increase in sensitivity while maintaining a false positives per case (FPs/case) of 0.63. For VNet, the FPs/case was reduced by 20 %, with a slight improvement in sensitivity. Compared to other guidance methods, our approach achieved optimal levels in various metrics and exhibited stronger robustness on unfamiliar datasets.

This study presents an artery fragment-guided approach that enhances the detection of cerebral aneurysms in TOF-MRA imaging. It not only outperforms our previous work but also excels when compared to alternative guidance methods. This approach offers a compelling knowledge-guided strategy for cerebral aneurysm detection.