Hippocampal volumetry for lateralization of temporal lobe epilepsy: automated versus manual methods

Abstract

The hippocampus has been the primary region of interest in the preoperative imaging investigations of mesial temporal lobe epilepsy (mTLE). Hippocampal imaging and electroencephalographic features may be sufficient in several cases to declare the epileptogenic focus. In particular, hippocampal atrophy, as appreciated on T1-weighted (T1W) magnetic resonance (MR) images, may suggest a mesial temporal sclerosis. Qualitative visual assessment of hippocampal volume, however, is influenced by head position in the magnet and the amount of atrophy in different parts of the hippocampus. An entropy-based segmentation algorithm for subcortical brain structures (LocalInfo) was developed and supplemented by both a new multiple atlas strategy and a free-form deformation step to capture structural variability. Manually segmented T1-weighted magnetic resonance (MR) images of 10 non-epileptic subjects were used as atlases for the proposed automatic segmentation protocol which was applied to a cohort of 46 mTLE patients. The segmentation and lateralization accuracies of the proposed technique were compared with those of two other available programs, HAMMER and FreeSurfer, in addition to the manual method. The Dice coefficient for the proposed method was 11% (p<10(-5)) and 14% (p<10(-4)) higher in comparison with the HAMMER and FreeSurfer, respectively. Mean and Hausdorff distances in the proposed method were also 14% (p<0.2) and 26% (p<10(-3)) lower in comparison with HAMMER and 8% (p<0.8) and 48% (p<10(-5)) lower in comparison with FreeSurfer, respectively. LocalInfo proved to have higher concordance (87%) with the manual segmentation method than either HAMMER (85%) or FreeSurfer (83%). The accuracy of lateralization by volumetry in this study with LocalInfo was 74% compared to 78% with the manual segmentation method. LocalInfo yields a closer approximation to that of manual segmentation and may therefore prove to be more reliable than currently published automatic segmentation algorithms.

Fig. 1

Flowchart of the proposed multiple-atlas segmentation method.

Fig. 2

Dice coefficients of the three methods of automatic segmentation (LocalInfo, HAMMER, FreeSurfer) by case number for all mTLE patients. The cases have been sorted according to the value of the Dice coefficient obtained for the proposed method (LocalInfo) in each case.

Fig. 3

Comparison of segmentation results for the hippocampus generated by LocalInfo (red), HAMMER (blue), and expert manual segmentation (white) in a series of coronal MR images of a sample mTLE case dataset.

Fig. 4

Comparison of segmentation results for the hippocampus generated by LocalInfo (red), FreeSurfer (green), and expert manual segmentation (white) in a series of coronal MR images of a sample mTLE case dataset. The average Dice coefficient for this case was 0.00 using FreeSurfer and 0.70 using LocalInfo.

Fig. 5

Comparison of segmentation results for the hippocampus generated by LocalInfo (red), HAMMER (blue), and expert manual segmentation (white) in a series of coronal MR images of a sample case with a poor segmentation outcome using HAMMER. The average Dice coefficients for this subject using LocalInfo and HAMMER were 0.62 and 0.39, respectively.

Fig. 6

Comparison of segmentation results for the hippocampus generated by LocalInfo (red), FreeSurfer (green), and expert manual segmentation (white) in a series of coronal MR images of a sample case with a poor segmentation outcome using FreeSurfer. The average Dice coefficients for this subject using LocalInfo and FreeSurfer were 0.70 and 0.00, respectively.

Fig. 7

Scatter plots of hippocampal volumes for (A) manual segmentation, (B) LocalInfo, (C) HAMMER, and (D) FreeSurfer. The symbols, ‘R’ and ‘L’, respectively, correspond to patients with right-and left-sided mTLE.