Automated hippocampal segmentation in patients with epilepsy: Available free online

Autor: E. Williams, John S. Duncan, Sebastien Ourselin, Jane L. Burdett, Miklos Espak, Charles Behr, Gavin P. Winston, M. Jorge Cardoso, Philippa A. Bartlett
Rok vydání: 2013
Předmět:
Zdroj: Epilepsia
ISSN: 0013-9580
DOI: 10.1111/epi.12408
Popis: The hippocampus is located within the medial temporal lobe and plays a key role in learning and episodic, semantic, and spatial memory. Dysfunction has been reported in neurologic and psychiatric disorders including epilepsy (Wu et al., 2005), Alzheimer's disease (Apostolova et al., 2006), schizophrenia (Tanskanen et al., 2005), and depression (Bremner et al., 2000). Temporal lobe epilepsy (TLE) is the most common drug-resistant focal epilepsy, with seizures frequently arising from the hippocampus. In surgical series of TLE, the pathology is often hippocampal sclerosis (HS) comprising neuronal loss and gliosis and marked by atrophy and signal change on magnetic resonance imaging (Van Paesschen, 2004). Atrophy of the hippocampus through HS provides a good biomarker for the laterality of the seizure focus (Bernasconi et al., 2003), and combined with concordant neurophysiology and neuropsychological data can be sufficient to recommend surgery. Hippocampal atrophy is associated with a favorable surgical outcome (Schramm & Clusmann, 2008). Visual assessment of hippocampal volumes is unreliable, as it may be compromised by head position and primarily detects hippocampal asymmetry rather than volume loss, making bilateral atrophy difficult to identify. Hippocampal segmentation and volumetry are thus important for diagnosis and surgical planning (Watson et al., 1997). The gold standard for hippocampal segmentation is manual delineation by trained raters. This is accurate, reproducible, and sensitive but is time-consuming, requires anatomic knowledge, and is subject to interrater and intrarater variability. The hippocampus is challenging to delineate as it is small and highly variable with ill-defined margins. Many protocols exist for manual segmentation depending on which structures are included and the boundary definition (Konrad et al., 2009). Automated segmentation techniques aim to ensure operator independence, high reproducibility, and reduced demand for human time and expertise. The strongest drive for automation has come from researchers working with large cohorts of patients with Alzheimer's disease patients. Hippocampal volumes are an early marker for the disease, are related to cognitive status, and may reflect disease progression in clinical trials (Frisoni & Jack, 2011). In atlas-based segmentation approaches, a template and associated manual labels are registered (matched) to the new image (Carmichael et al., 2005). Commonly used methods, including FreeSurfer (Fischl et al., 2002), rely on a single template so that subjects that differ significantly from the template, for example HS, are poorly segmented. Segmentation of hippocampi that are sclerotic is more challenging than segmenting hippocampi in Alzheimer's disease, as the latter is associated with more prominent cerebrospinal fluid (CSF)–hippocampal boundaries, whereas the former is associated with signal change. The use of an atlas with multiple template images is more effective than a single template (Heckemann et al., 2006) and depends on the quality of registration and template selection strategy. Most previous atlas-based segmentation studies used small template databases of healthy subjects. Results obtained in TLE are significantly worse than in healthy subjects or Alzheimer's disease (Kim et al., 2012), as aside from atrophy, approximately 40% of patients with TLE demonstrate an atypical shape or position of the hippocampus (Bernasconi et al., 2005). In this study, we adapted our published method developed for use in Alzheimer's disease (Cardoso et al., 2013) to a large cohort of adult patients with epilepsy by employing accurate nonlinear registration (Modat et al., 2010) and a large template database that encompasses the range of pathology observed in epilepsy at a tertiary referral center. Manual segmentations of the most similar images from the template database are combined using a label fusion strategy based on local similarity to ensure accurate segmentation regardless of pathology. We demonstrate that this technique achieves reliable segmentation with no more variability than that seen between different expert raters. The algorithm is made freely available via an online Web-based service (https://hipposeg.cs.ucl.ac.uk). In addition, the software, scripts, and an anonymized version of the template database are available from this website.
Databáze: OpenAIRE
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