Evaluation of Hippocampal Segmentation Methods for Healthy and Pathological Subjects

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dc.contributor.authorBishop, Courtney A.en_US
dc.contributor.authorJenkinson, Marken_US
dc.contributor.authorDeclerck, Jeromeen_US
dc.contributor.authorMerhof, Doriten_US
dc.contributor.editorDirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preimen_US
dc.date.accessioned2014-01-29T17:08:59Z
dc.date.available2014-01-29T17:08:59Z
dc.date.issued2010en_US
dc.description.abstractHippocampal atrophy is a clinical biomarker of Alzheimer's disease (AD) and is implicated in many other neurological and psychiatric diseases. For this reason, there is much interest in the accurate, reproducible delineation of this region of interest (ROI) in structural MR images. Here, both current and novel MR hippocampal segmentation methods are presented and evaluated: Two versions of FMRIB's Integrated Registration and Segmentation Tool (FIRST and FIRSTv2), Freesurfer's Aseg (FS), Classifier Fusion (CF) and a Fast Marching approach (FMClose). Segmentation performance on two clinical datasets is assessed according to three common measures: Dice coefficient, false positive rate (FPR) and false negative rate (FNR). The first clinical dataset contains 9 normal controls (NC) and 8 highly-atrophied AD patients, whilst the second is a collection of 16 NC and 16 bipolar (BP) patients. Results show that CF outperforms all other methods on the BPSA data, whilst FIRST and FIRSTv2 perform best on the CMA data, with average Dice coefficients of 0.81+-0.01, 0.85+-0.00 and 0.85+-0.01, respectively. This work brings to light several strengths and weaknesses of the evaluated hippocampal segmentation methods, of utmost importance for robust and accurate segmentation in the presence of specific and substantial pathology.en_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.identifier.isbn978-3-905674-28-6en_US
dc.identifier.issn2070-5786en_US
dc.identifier.urihttps://doi.org/10.2312/VCBM/VCBM10/017-024en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.6 [Image processing and computer vision]: Segmentation - Edge and feature detection, Region growing, partitioningen_US
dc.titleEvaluation of Hippocampal Segmentation Methods for Healthy and Pathological Subjectsen_US
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