Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Hippocampal 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. © The Eurographics Association 2010.

Original publication

DOI

10.2312/VCBM/VCBM10/017-024

Type

Journal article

Journal

EG VCBM 2010 - Eurographics Workshop on Visual Computing for Biology and Medicine

Publication Date

01/12/2010

Pages

17 - 24