  Although normal homologous brain structures are approximately symmetrical by
definition, they also have shape differences due to e.g. natural ageing. On the
other hand, neurodegenerative conditions induce their own changes in this
asymmetry, making them more pronounced or altering their location. Identifying
when these alterations are due to a pathological deterioration is still
challenging. Current clinical tools rely either on subjective evaluations,
basic volume measurements or disease-specific deep learning models. This paper
introduces a novel method to learn normal asymmetry patterns in homologous
brain structures based on anomaly detection and representation learning. Our
framework uses a Siamese architecture to map 3D segmentations of left and right
hemispherical sides of a brain structure to a normal asymmetry embedding space,
learned using a support vector data description objective. Being trained using
healthy samples only, it can quantify deviations-from-normal-asymmetry patterns
in unseen samples by measuring the distance of their embeddings to the center
of the learned normal space. We demonstrate in public and in-house sets that
our method can accurately characterize normal asymmetries and detect
pathological alterations due to Alzheimer's disease and hippocampal sclerosis,
even though no diseased cases were accessed for training. Our source code is
available at https://github.com/duiliod/DeepNORHA.
