A deep multimodal fusion framework for MRI-based segmentation of intracranial arterial calcification

Motivation: Recently, MRI-based intracranial arterial calcification segmentation has got increasing interest due to its clinical value, but current approaches to this challenging problem suffer from poor performance. Goal(s): To develop a deep learning model for enhancing calcification segmentation on MRI by using CT as additional training resource. Approach: A dissimilarity loss is proposed to align the latent features learned from MRI and CT of the same subject, thus making MR feature simpler and it easier for segmentation. Results: Compared with several commonly used segmentation networks, our model demonstrates superior performance in calcification segmentation. The ablation study further shows the effectiveness of the dissimilarity loss. Impact: The proposed model could be applied in clinical scenarios to automatically segment calcification on cerebral MR scans and it does not require CT imaging. Radiologists could leverage the segmentation result in the analysis of various vessel plaque components.