Automatic cerebral microbleeds detection from MR images via Independent Subspace Analysis based hierarchical features.

With the development of susceptibility weighted imaging (SWI) technology, cerebral microbleed (CMB) detection is increasingly essential in cerebrovascular diseases diagnosis and cognitive impairment assessment. Clinical CMB detection is based on manual rating which is subjective and time-consuming with limited reproducibility. In this paper, we propose a computer-aided system for automatic detection of CMBs from brain SWI images. Our approach detects the CMBs within three stages: (i) candidates screening based on intensity values (ii) compact 3D hierarchical features extraction via a stacked convolutional Independent Subspace Analysis (ISA) network (iii) false positive candidates removal with a support vector machine (SVM) classifier based on the learned representation features from ISA. Experimental results on 19 subjects (161 CMBs) achieve a high sensitivity of 89.44% with an average of 7.7 and 0.9 false positives per subject and per CMB, respectively, which validate the efficacy of our approach.