A scalable method to improve gray matter segmentation at ultra high field MRI

Abstract

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

Effects of MP2RAGE B1+sensitivity on inter-site T1reproducibility and morphometry at 7T

“…Segmentations for each of the ROIs were compared before and after B 1 + correction. This was based on the volumetric labels using total volume (in mm 3 ), label overlap (i.e., Dice) and distance between label boundaries (i.e., Hausdorff distance) as in Gulban et al (2018), and in surface space, based on shape differences using large deformation diffeomorphic metric mapping (LDDMM) as in Khan et al (2019). See also Figure 2 for a schematic overview of the processing workflow.…”

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: Effects on signal quality and sensitivity

“…Probabilistic functional maps were created separately for each participant and mental-imagery task following a leave-one-subject-out procedure 41 42 ) and manually corrected when necessary. The latter was done using ITK-snap 43 and BrainVoyager QX.…”

Depth-resolved ultra-high field fMRI reveals feedback contributions to surface motion perception

“…Initial tissue type segmentations was created with FSL FAST (Zhang, Brady, & Smith, 2001). These initial segmentations were semi-automatically improved using the Segmentator software (Gulban, Schneider, Marquardt, Haast, & De Martino, 2018) and ITK-SNAP (Yushkevich et al, 2006). These corrections of the segmentations obtained from FSL FAST were based on the T1 image from the MP2RAGE sequence, and aimed to remove mistakes in the definition of the white/grey matter boundary and at the pial surface.…”