  We present a novel neural network architecture called AutoAtlas for fully
unsupervised partitioning and representation learning of 3D brain Magnetic
Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network
components: one neural network to perform multi-label partitioning based on
local texture in the volume, and a second neural network to compress the
information contained within each partition. We train both of these components
simultaneously by optimizing a loss function that is designed to promote
accurate reconstruction of each partition, while encouraging spatially smooth
and contiguous partitioning, and discouraging relatively small partitions. We
show that the partitions adapt to the subject specific structural variations of
brain tissue while consistently appearing at similar spatial locations across
subjects. AutoAtlas also produces very low dimensional features that represent
local texture of each partition. We demonstrate prediction of metadata
associated with each subject using the derived feature representations and
compare the results to prediction using features derived from FreeSurfer
anatomical parcellation. Since our features are intrinsically linked to
distinct partitions, we can then map values of interest, such as
partition-specific feature importance scores onto the brain for visualization.
