  In this study, we proposed and validated a multi-atlas guided 3D fully
convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions
of interest (ROIs) from structural magnetic resonance images (MRIs). One major
limitation of existing state-of-the-art 3D FCN segmentation models is that they
often apply image patches of fixed size throughout training and testing, which
may miss some complex tissue appearance patterns of different brain ROIs. To
address this limitation, we trained a 3D FCN model for each ROI using patches
of adaptive size and embedded outputs of the convolutional layers in the
deconvolutional layers to further capture the local and global context
patterns. In addition, with an introduction of multi-atlas based guidance in
M-FCN, our segmentation was generated by combining the information of images
and labels, which is highly robust. To reduce over-fitting of the FCN model on
the training data, we adopted an ensemble strategy in the learning procedure.
Evaluation was performed on two brain MRI datasets, aiming respectively at
segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The
segmentation results of the proposed method were compared with those of a
state-of-the-art multi-atlas based segmentation method and an existing 3D FCN
segmentation model. Our results suggested that the proposed method had a
superior segmentation performance.
