Hippocampus Segmentation in MR image based on Atlas Registration and Broad Learning

Automatic segmentation of hippocampus in MRI plays an important role in computer aided diagnosis, which is helpful for early treatments of many diseases such as Alzheimers disease and epilepsy. In this paper, a coarse to fine hippocampus segmentation method is proposed. In coarse segmentation stage, atlas registration is applied to construct probabilistic atlases. In fine segmentation stage, atlas label images and coarse segmentation results are applied to delineate ROI. Furthermore, training samples extracted from ROI are used to train the broad learning model. Experimental results show that the proposed method is competitive with deep learning. The average Dice value achieves 0.83 on hippocampus segmentation, which indicates the superiority of the proposed method.