Description: This project is aimed at the automatic detection of focal cortical dysplastic regions from surface based morphometric data. Focal cortical dysplasia (FCD) is the most common cause of treatment resistant epilepsy in pediatrics and the second most cause for adults. Even with great advances in MRI 60% of FCD cases remain undetected in routine MRI visual inspection by experienced radiologists. Using co-registered cortical surfaces and a number of morphological features for both patients and normal controls we apply machine learning methods to predict potential FCD lesions.
Collaborators: Dr. Thomas Thesen and Dr. Karen E. Blackmon, Neurology, NYU Medical School.
Authors: Bilal Ahmed, Carla E. Brodley, Karen E. Blackmon, Ruben Kuzniecky, Gilad Barash, Chad Carlson, Brian T. Quinn, Werner Doyle, Jacqueline French, Orrin Devinsky, Thomas Thesen
Epilepsy & Behavior
Abstract: Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies (‘MRI-negative’), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as “lesional” when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions.
Authors: Bilal Ahmed, Thomas Thesen, Karen Blackmon, Yijun Zhao, Orrin Devinsky, Ruben Kuzniecky, Carla E. Brodley
Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: We cast the problem of detecting and isolating regions of abnormal cortical tissue in the MRIs of epilepsy patients in an image segmentation framework. Employing a multiscale approach we divide the surface images into segments of different sizes and then classify each segment as being an outlier, by comparing it to the same region across controls. The final classification is obtained by fusing the outlier probabilities obtained at multiple scales using a tree-structured hierarchical conditional random field (HCRF). The proposed method correctly detects abnormal regions in 90\% of patients whose abnormality was detected via routine visual inspection of their clinical MRI. More importantly, it detects abnormalities in 80\% of patients whose abnormality escaped visual inspection by expert radiologists.