Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy.
OBJECTIVE
To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome.


METHODS
We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization.


RESULTS
The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients.


SIGNIFICANCE
While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.
['Hadar|Peter N|PN|', 'Kini|Lohith G|LG|', 'Coto|Carlos|C|', 'Piskin|Virginie|V|', 'Callans|Lauren E|LE|', 'Chen|Stephanie H|SH|', 'Stein|Joel M|JM|', 'Das|Sandhitsu R|SR|', 'Yushkevich|Paul A|PA|', 'Davis|Kathryn A|KA|']
D000293:Adolescent D000328:Adult D000368:Aged D004833:Epilepsy, Temporal Lobe D005260:Female D006624:Hippocampus D006801:Humans D007091:Image Processing, Computer-Assisted D008279:Magnetic Resonance Imaging D008297:Male D008875:Middle Aged D012640:Seizures D013702:Temporal Lobe D055815:Young Adult
Automated Segmentation Hippocampus TLE