2 research outputs found
Adaptive processing of thin structures to augment segmentation of dual-channel structural MRI of the human brain
This thesis presents a method for the segmentation of dual-channel structural magnetic
resonance imaging (MRI) volumes of the human brain into four tissue classes. The
state-of-the-art FSL FAST segmentation software (Zhang et al., 2001) is in widespread
clinical use, and so it is considered a benchmark. A significant proportion of FAST’s
errors has been shown to be localised to cortical sulci and blood vessels; this issue has
driven the developments in this thesis, rather than any particular clinical demand.
The original theme lies in preserving and even restoring these thin structures,
poorly resolved in typical clinical MRI. Bright plate-shaped sulci and dark tubular
vessels are best contrasted from the other tissues using the T2- and PD-weighted data,
respectively. A contrasting tube detector algorithm (based on Frangi et al., 1998) was
adapted to detect both structures, with smoothing (based on Westin and Knutsson,
2006) of an intermediate tensor representation to ensure smoothness and fuller coverage
of the maps.
The segmentation strategy required the MRI volumes to be upscaled to an artificial
high resolution where a small partial volume label set would be valid and the segmentation
process would be simplified. A resolution enhancement process (based on Salvado
et al., 2006) was significantly modified to smooth homogeneous regions and sharpen
their boundaries in dual-channel data. In addition, it was able to preserve the mapped
thin structures’ intensities or restore them to pure tissue values. Finally, the segmentation
phase employed a relaxation-based labelling optimisation process (based on Li
et al., 1997) to improve accuracy, rather than more efficient greedy methods which are
typically used. The thin structure location prior maps and the resolution-enhanced data
also helped improve the labelling accuracy, particularly around sulci and vessels.
Testing was performed on the aged LBC1936 clinical dataset and on younger brain
volumes acquired at the SHEFC Brain Imaging Centre (Western General Hospital,
Edinburgh, UK), as well as the BrainWeb phantom. Overall, the proposed methods
rivalled and often improved segmentation accuracy compared to FAST, where the
ground truth was produced by a radiologist using software designed for this project.
The performance in pathological and atrophied brain volumes, and the differences with
the original segmentation algorithm on which it was based (van Leemput et al., 2003),
were also examined. Among the suggestions for future development include a soft labelling
consensus formation framework to mitigate rater bias in the ground truth, and
contour-based models of the brain parenchyma to provide additional structural constraints