Abstract
Statistical shape models are one of the most powerful methods in medical image segmentation problems. However, if the task is to segment complex structures, they are often too constrained to capture the full amount of anatomical variation. This is due to the fact that the number of training samples is limited in general, because generating hand-segmented reference data is a tedious and time-consuming task. To circumvent this problem, we present a Locally Deformable Statistical Shape Model that is able to segment complex structures with only a few training samples at hand. This is achieved by allowing a unique solution in each contour point. Unlike previous approaches, trying to tackle this problem by partitioning the statistical model, we do not need predefined segments. Smoothness constraints ensure that the local solution is restricted to the space of feasible shapes. Very promising results are obtained when we compare our new approach to a global fitting approach.
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Last, C., Winkelbach, S., Wahl, F.M., Eichhorn, K.W.G., Bootz, F. (2011). A Locally Deformable Statistical Shape Model. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_7
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DOI: https://doi.org/10.1007/978-3-642-24319-6_7
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