  Machine learning is playing an increasingly important role in medical image
analysis, spawning new advances in the clinical application of neuroimaging.
There have been some reviews on machine learning and epilepsy before, and they
mainly focused on electrophysiological signals such as electroencephalography
(EEG) and stereo electroencephalography (SEEG), while neglecting the potential
of neuroimaging in epilepsy research. Neuroimaging has its important advantages
in confirming the range of the epileptic region, which is essential in
presurgical evaluation and assessment after surgery. However, it is difficult
for EEG to locate the accurate epilepsy lesion region in the brain. In this
review, we emphasize the interaction between neuroimaging and machine learning
in the context of epilepsy diagnosis and prognosis. We start with an overview
of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI,
DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine
learning methods to neuroimaging data: i) the conventional machine learning
approach combining manual feature engineering and classifiers, ii) the deep
learning approach, such as the convolutional neural networks and autoencoders.
Subsequently, the application of machine learning on epilepsy neuroimaging,
such as segmentation, localization, and lateralization tasks, as well as tasks
directly related to diagnosis and prognosis are looked into in detail. Finally,
we discuss the current achievements, challenges, and potential future
directions in this field, hoping to pave the way for computer-aided diagnosis
and prognosis of epilepsy.
