Automated Detection of Cerebral Microbleeds on MR Images Using Knowledge Distillation Framework

Cerebral microbleeds (CMBs) are associated with white matter damage, various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate detection of CMBs would help to determine the CMB lesion count and distribution, which would be further useful to understand the clinical impact of CMBs and to obtain quantitative imaging biomarkers. In this work, we propose a fully automated, deep learning-based, 2-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g. GRE / SWI / QSM) for their accurate detection. Our method consists of an initial candidate detection step, that detects CMBs with high sensitivity and a candidate discrimination step using a knowledge distillation framework to classify CMB and non-CMB instances, followed by a morphological clean-up step. We used 4 datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) over 90% with an average of less than 2 false positives per subject. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and a better cluster-wise precision compared to existing state-of-the-art methods. When evaluated across different datasets, our method showed good generalisability with a cluster-wise TPR greater than 80% with different modalities.