Computational neuroscienceAmygdalar and hippocampal volume: A comparison between manual segmentation, Freesurfer and VBM
Introduction
Volumetric measurement of hippocampal or amygdalar volume is not only of interest because it reflects physiological processes but it might also gain clinical significance as a neuroimaging biomarker for diagnosis and prognostic evaluation.
Changes in hippocampal and amygdalar volumes have been reported in the literature for several mental and neurological disorders. Loss of hippocampal volume has been described for epilepsy (Tasch et al., 1999) and for Alzheimers’ disease, loss of hippocampal volume has been discussed as a diagnostic biomarker. Other disorders include depression or PTSD (Bremner et al., 1995, Bremner et al., 1997), where the neurotoxic effect of stress-related glucocorticoid excretion has been discussed. Changes in amygdalar volume have also been implicated in fear memory. Thus, changes in amygdalar volumes have been discussed for PTSD (Rogers et al., 2009), obsessive-compulsive disorder (Szeszko et al., 2004) or borderline personality disorder (Ruocco et al., 2012). As these studies were conducted in a clinical context, a conclusion which has often been drawn from these results is the derivation of independent diagnostic markers.
With data sets becoming larger, the need for automated instead of time-consuming manual segmentation has emerged. Several software packages enable such an automated estimation of volume with Freesurfer, FSL (FMRIB Software Library) or VBM (voxel-based morphometry, namely the MATLAB based toolbox VBM8) being among the most popular. There have been several methodological studies looking at the effects of different image-processing strategies for segmentation in general or comparing different software packages among each other or a manual “gold standard”. Most of these studies concentrated on the comparison manual versus automated segmentation of the hippocampus or the amygdala in young and healthy subjects with correlation coefficients ranging from 0.6 to 0.9 (Wenger et al., 2014, Klauschen et al., 2009, Nugent et al., 2012), strongly depending on the type of image processing and the brain region involved. Some studies compared whether automated or manual segmentation maximized group differences in mental and neurological disorders. In a recent genetic meta-analysis hippocampal volume estimates from different sources and methods from 5000 participants were pooled to find a genomic association, illustrating the need to understand the agreement between different measures (Stein et al., 2012).
Although comparative studies exist, our study adds novel aspects to the question of comparability. First, most studies on methodological comparisons concentrated on Pearson's correlation coefficient or related intra class coefficients (ICCs). Here we have also added methods that more adequately capture details of the methods comparisons involving Bland–Altman plots, Passing–Bablok regression and Lin's concordance coefficient to test the methods against the gold standard (manual segmentation). Second, as these software packages have been implemented with new algorithms in recent versions, it is interesting to examine under scientific “everyday” conditions the performance of the latest version. VBM8 employs innovative Markov-Random-Fields (MRF) and a high-dimensional non-linear warping, the latest FreeSurfer version realizes, for example, skull stripping with graph cuts available and uses extensive “look-up-tables”. Third, as we had access to a large (n = 92) set of manually segmented amygdalae and hippocampi, we were interested in comparing two widely used software packages (FreeSurfer and VBM8) to manual segmentation. This is especially interesting because most methodological studies compare FreeSurfer with FSL. VBM is most often utilized in the context of statistical parametric mapping (SPM) for voxel-wise analysis (Ashburner and Friston, 2000). However, it is possible to use this workflow for calculation of region-based volumes. As this might be an interesting alternative strategy for users, we provide an exemplary comparison to the Freesurfer V5.0 package. Finally, most other studies reported on smaller samples when manual segmentation was used (cf. Table 3).
Section snippets
Participants
Ninety-two participants were recruited as part of an ongoing study on predictors of posttraumatic stress disorder in not yet trauma-exposed individuals (paramedics at the beginning of their training). Participants shared a common educational background and were in the same age range (18–34 years; mean: 21.64; standard deviation: 2.57). Data on this sample were reported earlier in a study on hippocampal volume and fear conditioning (Pohlack et al., 2012). Subjects with mental disorder as
Visualization with Bland–Altman plots
The Bland–Altman charts and scatterplots (see right side of Fig. 1) allow a simple qualitative inspection of scattering and distortion of the data, as well as inspection of outliers. We restricted them to VBM8 versus manual segmentation of the right hemisphere, as the left hemisphere and Freesurfer did show the same pattern when inspected visually. As the Bland–Altman plots showed a deviation of the differential value (delta on the y-axis) from zero, this indicates a systematic difference
Discussion
The purpose of this study was to compare manual and automatic segmentation of hippocampus and amygdala. In addition, we present and discuss two alternative methods for segmentation (VBM8) and agreement analysis (CCC). Automated segmentation techniques are heterogeneous. In model-based segmentation methods, an MRI atlas that was previously manually labeled by an expert rater is matched to target images using nonlinear registration methods. The resulting nonlinear transformation is applied to the
Acknowledgments
This work was supported by grant of the Deutsche Forschungsgemeinschaft to HF (SFB636/C1).
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