Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images

Abstract

We seek to examine the use of an image processing pipeline on Magnetic Resonance Imaging (MRI) to identify features of Focal Cortical Dysplasia (FCD) in children who were suspected to have FCD on MRI (MRI-positive) and those with MRI-negative epilepsy. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. We implemented a modified version of the 2-Step Bayesian classifier method to a paediatric cohort with medically intractable epilepsy with MRI-positive and MRI-negative epilepsy, and evaluated the performance of the algorithm trained on textural features derived from T1-weighted (T1-w), T2-weighted (T2-w), and FLAIR (Fluid Attenuated Inversion Recovery) sequences. For MRI-positive cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (94% vs. 90% vs. 71% respectively), and also the highest lesional sensitivity (63% vs. 60% vs. 42% respectively), but the lowest lesional specificity (75% vs. 80% vs. 89% respectively). Combination of all three sequences improved the performance of the algorithm, with 97% subjectwise sensitivity. For MRI-negative cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (48% vs. 30% vs. 39% respectively), and also the highest lesional sensitivity (31% vs. 22% vs. 28% respectively). However, T2-w has the highest lesional specificity relative to T1-w and FLAIR (95% vs. 94% vs. 92% respectively) for MRI-negative cases. Combination of all three sequences improved the performance of the algorithm, with 70% subjectwise sensitivity. The 2-Step Naïve Bayes classifier correctly rejected 100% of the healthy subjects for all three sequences. Using combined morphometric and textural analysis in a 2-Step Bayesian classifier, applied to multiple MRI sequences, can assist with lesion detection in children with intractable epilepsy.

Keywords: Computer-aided diagnosis; Epilepsy; Focal cortical dysplasia; Morphometric and textural analysis.

Fig. 1

(a) There is thickening of the cortex in the left inferior frontal gyrus (arrow), in keeping with focal cortical dysplasia. Lesion labelling on (b and c) inflated and (d and e) pial surfaces.

Fig. 2

Contrast volumes using D = 3 for 13 directions and resulting C_avg. To generate symmetric 3-D GLCMs, 13 directions are considered. For each direction a matrix is generated and the contrast is computed. This value gets mapped back to the original voxel around which the GLCMs were generated. The average of the 13 values produced considering each direction gets mapped to the same location in C_avg. Doing so for all points produces the average Contrast volume. A sample axial slice of each direction and the generated average for Contrast is displayed.

Fig. 3

Experimental setup.

Fig. 4

MRI-positive case. (a) Axial T1 image shows the lesion in the right parietal lobe, with increased T1 signal and blurring of the gray-white matter junction. (b) Axial T1 slice with FreeSurfer regions using 2-Step Naive Bayes Classification method with all selected structures in green.

Fig. 5

MRI-negative case. (a) Axial T1 image shows no abnormality seen by visual assessment, and (b) the surgical resection site. (c) Axial T1 slice with selected FreeSurfer regions using 2-Step Naive Bayes classification method with all selected structures in green, which colocalize to the surgical resection site.