Journal
EPILEPSIA
Volume 54, Issue 12, Pages 2166-2173Publisher
WILEY-BLACKWELL
DOI: 10.1111/epi.12408
Keywords
Hippocampal segmentation; Hippocampal sclerosis; Epilepsy surgery; Magnetic resonance imaging
Categories
Funding
- Medical Research Council [G0802012]
- Engineering and Physical Sciences Research Council [EP/H046410/1]
- Comprehensive Biomedical Research Centre Strategic Investment Award [168]
- Wolfson Trust
- Epilepsy Society
- National Institute for Health Research University College London Hospitals Biomedical Research Centre
- EPSRC [EP/H046410/1] Funding Source: UKRI
- MRC [G0802012] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H046410/1] Funding Source: researchfish
- Medical Research Council [G0802012] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0509-10161] Funding Source: researchfish
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Purpose Hippocampal sclerosis, a common cause of refractory focal epilepsy, requires hippocampal volumetry for accurate diagnosis and surgical planning. Manual segmentation is time-consuming and subject to interrater/intrarater variability. Automated algorithms perform poorly in patients with temporal lobe epilepsy. We validate and make freely available online a novel automated method. MethodsManual hippocampal segmentation was performed on 876, 3T MRI scans and 202, 1.5T scans. A template database of 400 high-quality manual segmentations was used to perform automated segmentation of all scans with a multi-atlas-based segmentation propagation method adapted to perform label fusion based on local similarity to ensure accurate segmentation regardless of pathology. Agreement between manual and automated segmentations was assessed by degree of overlap (Dice coefficient) and comparison of hippocampal volumes. Key FindingsThe automated segmentation algorithm provided robust delineation of the hippocampi on 3T scans with no more variability than that seen between different human raters (Dice coefficients: interrater 0.832, manual vs. automated 0.847). In addition, the algorithm provided excellent results with the 1.5T scans (Dice coefficient 0.827), and automated segmentation remained accurate even in small sclerotic hippocampi. There was a strong correlation between manual and automated hippocampal volumes (Pearson correlation coefficient 0.929 on the left and 0.941 on the right in 3T scans). SignificanceWe demonstrate reliable identification of hippocampal atrophy in patients with hippocampal sclerosis, which is crucial for clinical management of epilepsy, particularly if surgical treatment is being contemplated. We provide a free online Web-based service to enable hippocampal volumetry to be available globally, with consequent greatly improved evaluation of those with epilepsy.
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