Knowledge-guided 2.5D CNN for cerebral microbleeds detection

Abstract

Cerebral microbleed (CMB) based on magnetic resonance imaging has been recently investigated as key biomarker in the diagnosis of cerebral small-vessel diseases and vascular cognitive impairment. Because the CMB lesions are typically small in size, and easily confused with various analogs such as calcified deposits, artifacts, and especially blood vessels when they are observed from a single MRI slice, reducing false positives in CMB detection is quite challenging. In addition, the lack of available medical image data, which inevitably leads to the imbalance between positive and negative samples, is also a challenge to existing deep learning algorithms. To address these problems, this paper proposes a simple but effective CMB detection method based on a novel deep architecture. First, in contrast to the current local patches-based approach, we make full use of the information about the distribution of CMBs in the whole brain based on training data as priori knowledge to guide the model to obtain candidate CMB patches. Second, we propose a 2.5D convolutional neural network based on morphological differences in cerebral blood vessels and cerebral microbleeds. Specifically, we further use information of the candidates in the coronal and sagittal planes and combine the inference based on three planes to determine the CMB probability of each patch. This approach strikes a balance between the high computational cost and the loss of spatial information. The effectiveness of the proposed method is demonstrated through experimental results that show that our KBPNet model has a sensitivity of 98.24%, an accuracy of 94.10% and an average number of false positives per patient of 1.72 on the SWI-CMB dataset.