  Recent advances in deep learning have improved the segmentation accuracy of
subcortical brain structures, which would be useful in neuroimaging studies of
many neurological disorders. However, most of the previous deep learning work
does not investigate the specific difficulties that exist in segmenting
extremely small but important brain regions such as the amygdala and its
subregions. To tackle this challenging task, a novel 3D Bayesian fully
convolutional neural network was developed to apply a dilated dualpathway
approach that retains fine details and utilizes both local and more global
contextual information to automatically segment the amygdala and its subregions
at high precision. The proposed method provides insights on network design and
sampling strategy that target segmentations of small 3D structures. In
particular, this study confirms that a large context, enabled by a large field
of view, is beneficial for segmenting small objects; furthermore, precise
contextual information enabled by dilated convolutions allows for better
boundary localization, which is critical for examining the morphology of the
structure. In addition, it is demonstrated that the uncertainty information
estimated from our network may be leveraged to identify atypicality in data.
Our method was compared with two state-of-the-art deep learning models and a
traditional multi-atlas approach, and exhibited excellent performance as
measured both by Dice overlap as well as average symmetric surface distance. To
the best of our knowledge, this work is the first deep learning-based approach
that targets the subregions of the amygdala.
