Automated hippocampal location and extraction
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Date
2010Author
Bonnici, Heidi M.
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Abstract
The hippocampus is a complex brain structure that has been studied extensively and
is subject to abnormal structural change in various neuropsychiatric disorders. The
highest definition in vivo method of visualizing the anatomy of this structure is
structural Magnetic Resonance Imaging (MRI). Gross structure can be assessed by
the naked eye inspection of MRI scans but measurement is required to compare scans
from individuals within normal ranges, and to assess change over time in individuals.
The gold standard of such measurement is manual tracing of the boundaries of the
hippocampus on scans. This is known as a Region Of Interest (ROI) approach. ROI
is laborious and there are difficulties with test-retest and inter-rater reliability. These
difficulties are primarily due to uncertainty in designation of the hippocampus
boundary. An improved, less labour intensive and more reliable method is clearly
desirable.
This thesis describes a fully automated hybrid methodology that is able to first locate
and then extract hippocampal volumes from 3D 1.5T MRI T1 brain scans
automatically. The hybrid algorithm uses brain atlas mappings and fuzzy inference to
locate hippocampal areas and create initial hippocampal boundaries. This initial
location is used to seed a deformable manifold algorithm. Rule based deformations
are then applied to refine the estimate of the hippocampus locations. Finally, the
hippocampus boundaries are corrected through an inference process that assures
adherence to an expected hippocampus volume. The ICC values of this methodology when compared to the manual segmentation of
the same hippocampi result in a 0.73 for the left and 0.81 for the right hippocampi.
These values both fall within the range of reliability testing according to the manual
‘gold standard’ technique. Thus, this thesis describes the development and validation
of a genuinely automated approach to hippocampal volume extraction of potential
utility in studies of a range of neuropsychiatric disorders and could eventually find
clinical applications.