With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives
for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The
median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.
We evaluate two core modules of a novel soft tissue navigation system. The system estimates the position of
a hidden target (e.g. a tumor) during a minimally invasive intervention from the location of a set of optically
tracked needle-shaped navigation aids which are placed in the vicinity of the target. The initial position of the
target relative to the navigation aids is obtained from a CT scan. The accuracy of the entire system depends on
(a) the accuracy for locating a set of navigation aids in a CT image, (b) the accuracy for determining the positions
of the navigation aids during the intervention by means of optical tracking, (c) the accuracy for tracking the
applicator (e.g. the biopsy needle), and (d) the accuracy of the real-time deformation model which continuously
computes the location of the initially determined target point from the current positions of the navigation aids.
In this paper, we focus on the first two aspects. We introduce the navigation aids we constructed for our
system and show that the needle tips can be tracked with submillimeter accuracy. Furthermore, we present and
evaluate three methods for registering a set of navigation aid models with a given CT image. The fully-automatic
algorithm outperforms both the manual method and the semi-automatic algorithm, yielding an average distance
of 0.27 ± 0.08 mm between the estimated needle tip position and the reference position.
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