A Mask R-CNN-Based Approach for Brain Aneurysm Detection and Segmentation from TOF-MRA Data.

Background

Accurate detection of intracranial aneurysms, especially those smaller than 3 mm, remains a critical challenge in neurovascular imaging due to their subtle morphology and low contrast in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) scans. This study presents a Mask R-CNN-based deep learning framework designed to automatically detect and segment intracranial aneurysms, with specific architectural modifications aimed at improving sensitivity to small lesions. Method: A dataset of 447 TOF-MRA volumes (161 aneurysmal, 286 healthy) was used, with patient-level deduplication and 5-fold cross-validation to ensure robust evaluation. Bayesian hyperparameter optimization was applied using Optuna, and two key innovations were introduced: a Small Object Aware ROI Head to better capture micro-aneurysms and customized anchor configurations to improve region proposal quality. Healthy scans were incorporated as negative samples to enhance background modeling, and targeted data augmentation increased model generalization.

Results

The proposed model achieved a Dice coefficient of 0.8832, precision of 0.9404, and sensitivity (recall) of 0.8677, with consistent performance across aneurysm sizes.

Conclusions

These results demonstrate that the integration of architectural innovations, automated optimization, and negative-sample modeling enables a clinically viable deep learning tool that could serve as a reliable second-reader system for assisting radiologists in intracranial aneurysm detection.