Anatomically-Guided Segmentation of Cerebral Microbleeds in T1-Weighted and T2*-Weighted MRI

Cerebral microbleeds (CMBs) are defined as relatively small blood depositions in the brain that serve as severity indicators of small vessel diseases, and thus accurate quantification of CMBs is clinically useful. However, manual annotation of CMBs is an extreme burden for clinicians due to their small size and the potential risk of misclassification. Moreover, the extreme class imbalance inherent in CMB segmentation tasks presents a significant challenge for training deep neural networks. In this paper, we propose to enhance CMB segmentation performance by introducing a proxy task of segmentation of supratentorial and infratentorial regions. This proxy task could leverage clinical prior knowledge in the identification of CMBs. We evaluated the proposed model using an in-house dataset comprising 335 subjects with 582 longitudinal cases and an external public dataset consisting of 72 cases. Our method performed better than other methods that did not consider proxy tasks. Quantitative results indicate that the proxy task is robust on unseen datasets and thus effective in reducing false positives. Our code is available at https://github.com/junmokwon/AnatGuidedCMBSeg.