Surface-based multi-template automated hippocampal segmentation: Application to temporal lobe epilepsy

In drug-resistant temporal lobe epilepsy (TLE), detecting hippocampal atrophy on MRI is crucial as it allows defining the surgical target. In addition to atrophy, about 40% of patients present with malrotation, a developmental anomaly characterized by atypical morphologies of the hippocampus and collateral sulcus. We have recently shown that both atrophy and malrotation impact negatively the performance of volume-based techniques. Here, we propose a novel hippocampal segmentation algorithm (SurfMulti) that integrates deformable parametric surfaces, vertex-wise modeling of locoregional texture and shape, and multiple templates in a unified framework. To account for inter-subject variability, including shape variants, we used a library derived from a large database of healthy (n = 80) and diseased (n = 288) hippocampi. To quantify malrotation, we generated 3D models from manual hippocampal labels and automatically extracted collateral sulci. The accuracy of SurfMulti was evaluated relative to manual labeling and segmentation obtained through a single atlas-based algorithm (FreeSurfer) and a volume-based multi-template approach (Vol-multi) using the Dice similarity index and surface-based shape mapping, for which we computed vertex-wise displacement vectors between automated and manual segmentations. We then correlated segmentation accuracy with malrotation features and atrophy. SurfMulti outperformed FreeSurfer and Vol-multi, and achieved a level of accuracy in TLE patients (Dice = 86.9%) virtually identical to healthy controls (Dice = 87.5%). Vertex-wise shape mapping showed that SurfMulti had an excellent overlap with manual labels, with sub-millimeter precision. Its performance was not influenced by atrophy or malrotation (|r| < 0.20, p > 0.2), while FreeSurfer (|r| > 0.35, p < 0.0001) and Vol-multi (|r| > 0.28, p < 0.05) were hampered by both anomalies. The magnitude of atrophy detected using SurfMulti was the closest to manual volumetry (Cohen’s d: manual = 1.71, t = 7.6; SurfMulti = 1.60, t = 7.0; Vol-multi = 1.38, t = 6.1; FreeSurfer = 0.91, t = 3.9). The high performance of SurfMulti regardless of cohort, atrophy and shape variants identifies this algorithm as a robust segmentation tool for hippocampal volumetry.

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► Automated surface-based multi-template hippocampal segmentation.
► Vertex-wise modeling of locoregional texture and shape.
► Higher performance compared to volume-based approaches.
► Accuracy not influenced by hippocampal shape variants or atrophy.