  Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in
children, which can be associated with seizures, developmental delay and motor
weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but
some cases can be challenging to detect even for experienced radiologists. In
this study, we create an open pediatric MRI dataset (PPMR) with PMG and
controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa,
Canada. The differences between PMG MRIs and control MRIs are subtle and the
true distribution of the features of the disease is unknown. This makes
automatic detection of cases of potential PMG in MRI difficult. We propose an
anomaly detection method based on a novel center-based deep contrastive metric
learning loss function (cDCM) which enables the automatic detection of cases of
potential PMG. Additionally, based on our proposed loss function, we customize
a deep learning model structure that integrates dilated convolution,
squeeze-and-excitation blocks and feature fusion for our PPMR dataset. Despite
working with a small and imbalanced dataset our method achieves 92.01% recall
at 55.04% precision. This will facilitate a computer aided tool for
radiologists to select potential PMG MRIs. To the best of our knowledge, this
research is the first to apply machine learning techniques to identify PMG from
MRI only.
