Here, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011; Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean = 0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean = 0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency = 0.830, absolute agreement = 0.819 for the left; consistency = 0.786, absolute agreement = 0.783 for the right) and bivariate (r = 0.831 for the left; r = 0.797 for the right) compared to hand-drawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed.
Keywords: amygdala, automated segmentation, structural MRI, amygdala volume, Freesurfer, medial temporal lobe, diffeomorphic warping, hand-tracing
Citation: Hanson JL, Suh JW, Nacewicz BM, Sutterer MJ, Cayo AA, Stodola DE, Burghy CA, Wang H, Avants BB, Yushkevich PA, Essex MJ, Pollak SD and Davidson RJ (2012) Robust automated amygdala segmentation via multi-atlas diffeomorphic registration. Front. Neurosci. 6:166. doi: 10.3389/fnins.2012.00166
Received: 10 September 2012; Paper pending published: 03 October 2012;
Accepted: 24 October 2012; Published online: 29 November 2012.
Edited by:Thomas J. Grabowski, University of Washington School of Medicine, USA
Reviewed by:Jennifer L. Robinson, Auburn University, USA
Copyright: © 2012 Hanson, Suh, Nacewicz, Sutterer, Cayo, Stodola, Burghy, Wang, Avants, Yushkevich, Essex, Pollak and Davidson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Jamie L. Hanson, Waisman Center and Department of Psychology, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI 53705, USA. e-mail: email@example.com