Surface-based automated FCD detection at GOSH

Automated Detection of Focal Cortical Dysplasias at GOSH using a surface-based approach

Focal cortical dysplasia (FCD) is a congenital abnormality of cortical development and a leading cause of surgically remediable drug resistant epilepsy. MRI has played a major role in the evaluation of patients; yet, significant proportions of lesions remain undetected by conventional image analysis. Machine learning offers a powerful framework to develop automated and individualised clinical tools that may improve the detection of lesions and prediction of clinically relevant outcome.

In work published in Neuroimage: Clinical in 2017, Adler, Wagstyl et al., developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort from Great Ormond Street Hospital. Focal cortical dysplasias in this paediatric cohort were correctly identified in 73% of the children.

FCD examples
Examples of cortical area detected by the neural network classifier in 5 patients with a radiological diagnosis of FCD. First column: T1-weighted images. Second column: FLAIR images. White circle on T1 and FLAIR images indicates lesion location. Third column: Neural network classifier output (yellow) and manual lesion mask (light blue) viewed on pial surface, for large lesions, or inflated surface, for small lesions buried in sulci.

Further studies have since validated validated this method: Jin et al., 2018, in Epilepsia, Mo et al., 2018, Frontiers in Neuroscience, and Wagstyl, Adler et al., Epilepsia