EfficientNet-B0 U-Net Model for Improved Specificity Cerebral Microbleed Detection on QSM MRI

Motivation: Cerebral microbleeds (CMBs) are critical markers of neurovascular health; yet their small size and low contrast hinder detection in MRI. This study leverages QSM MRI and deep learning to improve CMB segmentation accuracy, aiming to reduce false positives. Goal(s): Develop a high-sensitivity, efficient model for CMB segmentation in QSM MRI, improving accuracy and workflow integration over conventional techniques. Approach: Using 2D U-Net with EfficientNet-B0, pseudo-3D stacking, and Focal and Dice Loss, we enhance CMB visibility and segmentation accuracy, validated on a custom dataset. Results: The model achieved a Dice score of 0.7290, sensitivity of 0.8171, and specificity of 0.9506, confirming reliable CMB segmentation. Impact: This model offers a sensitive, but highly specific automated CMB detection method, well suited to augment human readers for the multiple clinical instances requiring careful CMB biomarker assessment. Its robust performance enables large-scale CMB studies, advancing neurovascular diagnostics and research.