Robust Automated Amygdala Segmentation via Multi-Atlas Diffeomorphic Registration - Hanson, Jamie; Suh, Jung; Nacewicz, Brendon; Sutterer, Matt; Cayo, Amy; Stodola, Diane; Burghy, Cory; Wang, Hongzhi; Avants, Brian; Yushkevich, Paul; Essex, Marilyn; Polllak, Seth; et al. (Frontiers in Neuroscience, 2012)
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; Wang et al., 2011b). 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 (consistency=.830, absolute agreement =.819 for the left; consistency=. 786, absolute agreement =. 783 for the right) and bivariate (r =.831 for the left; r =0.797 for the right) correlations compared to hand-drawn amygdala. Our results are discussed in relation to results from 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.