Lateralization and prognosis of temporal lobe epilepsy patients by shape analysis of hippocampus via signed poisson mapping
Shape analysis of three-dimensional (3D) brain structures has facilitated the study of discrepancies between patients suffering from neurological diseases and normal subjects. Not only does shape analysis help physicians in qualitative assessments but also can be used to build a computer assisted model based on the quantification results. In this study, we focus on statistical analysis of shape features for temporal lobe epilepsy (TLE) lateralization and outcome prediction. First, we evaluate the role of an external atlas as a sharp mean reference shape for shape analysis using Signed Poisson Mapping (SPoM). Then, we extract shape features from the hippocampi of TLE patients and use them for lateralization using various machine learning methods. We also analyze surgery outcome of TLE patients based on Engel classification relative to the shape features of their hippocampi. Results showed no significant differences between statistical outcomes of the atlas based shape reference and the conventional mean shape. However, among the synthetic shapes, statistical significance level on the atlas based reference shape recovered along its principal axis was superior. In terms of machine learning methods for lateralization, linear Support Vector Machine (SVM) outshined other algorithms using wrapper and filter based feature selection schemes. Finally, surgery outcome of patients in terms of hippocampus deformations in different Engel classes showed no statistically significant results.