Abstract
PurposeHippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.Materials and MethodsWe used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).ResultsDeep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.ConclusionOur findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.
Highlights
Magnetic resonance imaging (MRI) is the key element in diagnosing structural lesions in epilepsy [1]
While hippocampal sclerosis (HS) in advanced stages is usually reliably identified in epilepsy specific MRI by experts [6], the challenge remains putative in non-lesional (MRI negative) patients in an early stage [7]
Rather than comparing with a ground truth expert segmentation, the present study aimed to examine the impact of the segmentation method on the end result of a clinically motivated question, in this case, quantifying hippocampal sclerosis in patients with epilepsy
Summary
Magnetic resonance imaging (MRI) is the key element in diagnosing structural lesions in epilepsy [1]. Its characteristic neuronal loss and gliosis manifesting as volume loss and increased T2 signal intensities [5] make MRI an essential clinical tool for the differential diagnosis in TLE. While HS in advanced stages is usually reliably identified in epilepsy specific MRI by experts [6], the challenge remains putative in non-lesional (MRI negative) patients in an early stage [7]. Quantitative hippocampal volumetry is already recommended for patients with TLE, who were considered for epilepsy surgery [8]. Manual segmentations are recommended [9], a labor-intensive task requiring specific training to achieve good inter-rater agreement [10]
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