Multi-template approaches for segmenting the hippocampus: the case of the SACHA software (original) (raw)
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.
Medical Image Computing …, 2011
The most frequent drug-resistant epilepsy is temporal lobe epilepsy (TLE) related to hippocampal atrophy. In addition, TLE is associated with atypical hippocampal morphologies. Automatic hippocampal segmentations have generally provided unsatisfactory results in this condition. We propose a novel segmentation method (SurfMulti) to statistically estimate locoregional texture and shape using a surface-based approach that guarantees shape-inherent point-wise correspondences. To account for inter-subject variability, including shape variants, we used a multi-template library derived from a large database of controls and patients. SurfMulti outperformed state-of-the-art volume-based single- and multi-template approaches, with performances comparable to controls (Dice index: 86.1 vs. 87.5%). Furthermore, the sensitivity of SurfMulti to detect atrophy was similar to that of manual volumetry. Given that the presence of hippocampal atrophy in TLE predicts a favorable seizure outcome after surgery, the proposed automated algorithm assures to be a robust surrogate tool in the presurgical evaluation for the time-demanding manual procedure.