Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention

Abstract

Quality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the assumption that statistical outliers are related to errors of cortical extension, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships. T1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores ±3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (non-outlier group). The outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size (Φ = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Although nine brain regions significantly changed size from pre- to post-editing (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05). Statistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing.

Alteration of perivascular spaces in early cognitive decline

“…Prior to parcellation, recon-all applies the following pre-processing steps: motion correction, nonuniform intensity normalization, Talairach transform computation, intensity normalization and skull stripping [52,53,[62][63][64][65][54][55][56][57][58][59][60][61].…”

Image processing approaches to enhance perivascular space visibility and quantification using MRI

“…We constructed a framework for PVS quantification and mapping using MRI, which can be applied to T1w, T2w, and EPC. Preprocessed data of HCP was used, which includes motion correction, non-uniform intensity normalization, Talairach transform computation, intensity normalization and skull stripping Desikan et al, 2006;Fischl et al, 2004bFischl et al, , 2004aFischl et al, , 2002Fischl et al, , 1999Fischl and Dale, 2000;Reuter et al, 2012Reuter et al, , 2010Reuter and Fischl, 2011;Segonne et al, 2007Segonne et al, , 2004Sled et al, 1998;Waters et al, 2018). Then nonlocal mean filtering was applied and MRI images were parcellated to extract masks of white matter and basal ganglia, using n-tissue parcellation technique of the Advanced Normalization Tools (ANTs) software package (Avants et al, 2011).…”

Alcoholism Gender Differences in Brain Responsivity to Emotional Stimuli

“…Images were registered with a canonical brain surface (fsaverage) based on sulcal and gyral patterns (B. Fischl, Sereno, Tootell, & Dale, 1999) , and registered with a canonical brain volume (MNI305) using a 12 degrees of freedom nonlinear transform. Gray and white matter surface accuracy were individually examined using automaticallygenerated quality control figures ( https://github.com/poldracklab/niworkflows ), and no errors were detected that would be likely to influence the outcomes of this project (Waters, Mace, Sawyer, & Gansler, 2018) .…”