Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy

dc.contributor.author Martins,C en
dc.contributor.author Nádia Moreira Silva en
dc.contributor.author Silva,G en
dc.contributor.author Rozanski,VE en
dc.contributor.author João Paulo Cunha en
dc.date.accessioned 2018-01-16T16:08:33Z
dc.date.available 2018-01-16T16:08:33Z
dc.date.issued 2016 en
dc.description.abstract Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the 'Advanced Brain Imaging Lab' (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician. © 2016 IEEE. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6423
dc.identifier.uri http://dx.doi.org/10.1109/embc.2016.7592178 en
dc.language eng en
dc.relation 5864 en
dc.relation 5875 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy en
dc.type conferenceObject en
dc.type Publication en
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