Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review

Machine learning is playing an increasing important role in medical image analysis, spawning new advances in neuroimaging clinical applications. However, previous work and reviews were mainly focused on the electrophysiological signals like EEG or SEEG; the potential of neuroimaging in epilepsy research has been largely overlooked despite of its wide use in clinical practices. In this review, we highlight the interactions between neuroimaging and machine learning in the context of the epilepsy diagnosis and prognosis. We firstly outline typical neuroimaging modalities used in epilepsy clinics, \textit{e.g} MRI, DTI, fMRI and PET. We then introduce two approaches to apply machine learning methods to neuroimaging data: the two-step compositional approach which combines feature engineering and machine learning classifier, and the end-to-end approach which is usually toward deep learning. Later a detailed review on the machine learning tasks on epileptic images is presented, such as segmentation, localization and lateralization tasks, as well as the tasks directly related to the diagnosis and prognosis. In the end, we discuss current achievements, challenges, potential future directions in the field, with the hope to pave a way to computer-aided diagnosis and prognosis of epilepsy.