Automated measurement of the human corpus callosum using MRI

Timothy J Herron, Xiaojian Kang, David L Woods, Timothy J Herron, Xiaojian Kang, David L Woods

Abstract

The corpus callosum includes the majority of fibers that connect the two cortical hemispheres. Studies of cross-sectional callosal morphometry and area have revealed developmental, gender, and hemispheric differences in healthy populations and callosal deficits associated with neurodegenerative disease and brain injury. However, accurate quantification of the callosum using magnetic resonance imaging is complicated by intersubject variability in callosal size, shape, and location and often requires manual outlining of the callosum in order to achieve adequate performance. Here we describe an objective, fully automated protocol that utilizes voxel-based images to quantify the area and thickness both of the entire callosum and of different callosal compartments. We verify the method's accuracy, reliability, robustness, and multisite consistency and make comparisons with manual measurements using public brain-image databases. An analysis of age-related changes in the callosum showed increases in length and reductions in thickness and area with age. A comparison of older subjects with and without mild dementia revealed that reductions in anterior callosal area independently predicted poorer cognitive performance after factoring out Mini-Mental Status Examination scores and normalized whole brain volume. Open-source software implementing the algorithm is available at www.nitrc.org/projects/c8c8.

Keywords: Alzheimer's disease; aging; colossal commissure; gender; genu; isthmus; morphometry; splenium.

Figures

Figure 1
Figure 1
Complexities in defining thickness (using dotted line segments) shown on a cartoon posterior callosum. (A) Problem with definition by minimal traversal distance (vertical line is shorter). (B) Line segments defining thickness cannot always be perpendicular to both boundaries simultaneously. (C) Sensitivity of thickness to median anchor point (solid lines) when perpendicularity to the median line is required. (D) High boundary curvature on only one surface causes fans of thickness-defining lines that nearly intersect; complicating attempts to define thickness using uniformly spaced grid lines.
Figure 2
Figure 2
Preprocessing of T1 images. Each T1-weighted MR image (A) was normalized into MNI space (B) and segmented into white matter (C) and gray matter (D). Labels: FX = fornix and PCA = peri-callosal arteries. Arrows indicate incorrectly segmented WM voxels at the callosal boundary in the GM partition.
Figure 3
Figure 3
Quantification of callosal area and thickness. (A) Average callosal structure of 152 young subjects from the OASIS database, averaged in MNI space. Each subject's callosum was subdivided into five compartments along the anterior–posterior axis using geometric ratios following Hofer and Frahm (H&F, top of panel A) (Hofer and Frahm, 2006) and divided into six compartments following Witelson (W, bottom of panel A) (Witelson, 1989). (B) Callosal boundaries were defined with reference to a series of radial lines (three shown) emanating from a centroid. (C) Radial lines intersecting the callosum were oriented vertically. This unwrapped the callosum to define a median line and measure thickness. The same three lines intersecting the callosum in (B) are shown. The light gray line shows the median location of WM probabilities (dark gray) considered vertically. Callosal thickness was computed at each point using the shortest line segment connecting the superior and inferior surfaces through that point (five shown, short thin white).
Figure 4
Figure 4
Mean and standard deviation (error bars) thickness at 50 equal angle spaced locations using automated segmentation (purple solid) and expert-corrected segmentations (gold dashed). The red dotted line indicates the Pearson correlations between the two values at each location.
Figure 5
Figure 5
Scatterplot comparing manually delineated corpus callosum total cross-sectional areas (y-axis) with C8 area estimates (x-axis) for 20 normal subjects in the OASIS database who underwent repeated scans. Diamonds are from image session 1 and asterisks are for session 2. Thin dotted lines connect results for the same subject's two sessions, while the thick dotted line is the area diagonal.
Figure 6
Figure 6
Mean mid-sagittal callosal thicknesses from 14 different scanners (black dotted lines) with young (age means 21–33 years), mixed gender, healthy subjects taken from the FCP database (see Supplemental Table S1). The thick gray line shows the mean and standard deviation.
Figure 7
Figure 7
Scatterplots of Age vs. medial callosal length (A) and Age vs. mean callosal thickness (B) for 1227 FCP database images processed by C8 (cyan/circles) as well as for both automated (purple/crosses) and expert-corrected (gold/triangles) segmentations of 316 OASIS control subjects. Estimated quadratic age regression lines are included.
Figure 8
Figure 8
Spearman correlation values for callosal thicknesses at 16 locations (Ant: anterior, Pos: posterior), regional callosal areas (within 5 H&F partitions: see Figure 3), and callosal length along with estimated GM and WM volumes, age and gender for the 1231 FCP subjects. Square areas are proportional to correlation values, both positive (black) and negative (red), with correlations of 1.0 on the diagonal for scale.

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Source: PubMed

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