Next to aneurysm size, aneurysm growth over time is an important indicator for aneurysm rupture risk. Manual
assessment of aneurysm growth is a cumbersome procedure, prone to inter-observer and intra-observer variability. In
clinical practice, mainly qualitative assessment and/or diameter measurement are routinely performed. In this paper a
semi-automated method for quantifying aneurysm volume growth over time in CTA data is presented. The method treats
a series of longitudinal images as a 4D dataset. Using a 4D groupwise non-rigid registration method, deformations with
respect to the baseline scan are determined. Combined with 3D aneurysm segmentation in the baseline scan, volume
change is assessed using the deformation field at the aneurysm wall. For ten patients, the results of the method are
compared with reports from expert clinicians, showing that the quantitative results of the method are in line with the
assessment in the radiology reports. The method is also compared to an alternative method in which the volume is
segmented in each 3D scan individually, showing that the 4D groupwise registration method agrees better with manual
assessment.
Accurately quantifying aneurysm shape parameters is of clinical importance, as it is an important factor in choosing the
right treatment modality (i.e. coiling or clipping), in predicting rupture risk and operative risk and for pre-surgical
planning. The first step in aneurysm quantification is to segment it from other structures that are present in the image. As
manual segmentation is a tedious procedure and prone to inter- and intra-observer variability, there is a need for an
automated method which is accurate and reproducible. In this paper a novel semi-automated method for segmenting
aneurysms in Computed Tomography Angiography (CTA) data based on Geodesic Active Contours is presented and
quantitatively evaluated. Three different image features are used to steer the level set to the boundary of the aneurysm,
namely intensity, gradient magnitude and variance in intensity. The method requires minimum user interaction, i.e.
clicking a single seed point inside the aneurysm which is used to estimate the vessel intensity distribution and to
initialize the level set. The results show that the developed method is reproducible, and performs in the range of interobserver
variability in terms of accuracy.
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