Deep convolutional neural networks for detection of cortical dysplasia: a multicenter validation
Focal cortical dysplasia (FCD) is a surgically-amenable epileptogenic developmental malformation. On MRI, FCD typically presents with cortical thickening, hyperintensity, and blurring of the gray-white matter interface. These changes may be visible to the naked eye, or subtle and be easily overlooked. Despite advances in MRI analytics, current surface-based algorithms do not detect FCD in >50% of FCD lesions.
We propose a novel algorithm to distinguish FCD from healthy tissue on MRI voxels. Our method harnesses feature learning capability of convolutional neural networks (CNN), a powerful deep learning paradigm. The algorithm* was trained and tested on data from the Montreal Neurological Institute (MNI) and tested on independent data from MNI and eight sites worldwide.
* Gill RS et al. Detection of MRI-negative focal cortical dysplasia using uncertainty-informed Bayesian deep learning: A multicentre validation study. Annual meeting of the American Epilepsy Society, Baltimore, MD, USA 2019.
* Gill RS et al. Uncertainty-informed detection of epileptogenic brain malformations using Bayesian neural networks. International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China 2019.
* Gill RS et al. Automatic detection of focal cortical dysplasia using deep learning: A multicentre validation study . Annual meeting of the American Epilepsy Society, Washington, DC, USA 2018.
* Gill RS et al. Deep convolutional networks for automated detection of epileptogenic brain malformations. International Conference on Medical Image Computing and Computer Assisted Intervention, Granada, Spain 2018.
* Gill RS et al. Deep convolutional neural networks for detection of cortical dysplasia: a multicenter validation. Annual meeting of the Organization for Human Brain Mapping, Singapore 2018.
