Brain segmentation based on multi-atlas and diffeomorphism guided 3D fully convolutional network ensembles

In this study, we proposed and validated a multi-atlas and diffeomorphism guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain anatomical regions of interest (ROIs) from structural magnetic resonance images (MRIs). A novel multi-atlas and diffeomorphism based encoding block and ROI patches with adaptive sizes were used. In the multi-atlas and diffeomorphism based encoding block, both MRI intensity profiles and expert priors from deformed atlases were encoded and fed to the proposed FCN. Utilizing patches with adaptive sizes enabled more efficient network training and testing. To incorporate both local and global contextual information of a specific ROI, we employed a long skip connection between the layer of the encoding block and the layer of the encoding-decoding block. To relieve over-fitting of the proposed FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Systematic evaluations were performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 whole-brain ROIs. Compared with two state-of-the-art segmentation methods including a multi-atlas based segmentation method and an existing 3D FCN segmentation model, the proposed method exhibited superior segmentation performance.