T Heimann1, I Wolf, H-P Meinzer. 1. Division of Medical and Biological Iniformatics, German Cancer Research Center, Heidelberg, Germany. t.heimann@dkfz.de
Abstract
OBJECTIVES: To point out the problem of non-uniform landmark placement in statistical shape modeling, to present an improved method for generating landmarks in the 3D case and to propose an unbiased evaluation metric to determine model quality. METHODS: Our approach minimizes a cost function based on the minimum description length (MDL) of the shape model to optimize landmark correspondences over the training set. In addition to the standard technique, we employ an extended remeshing method to change the landmark distribution without losing correspondences, thus ensuring a uniform distribution over all training samples. To break the dependency of the established evaluation measures generalization and specificity from the landmark distribution, we change the internal metric from landmark distance to volumetric overlap. RESULTS: Redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry. CONCLUSIONS: The distribution of landmarks on the training shapes is -- beyond the correspondence issue -- a crucial point in model construction.
OBJECTIVES: To point out the problem of non-uniform landmark placement in statistical shape modeling, to present an improved method for generating landmarks in the 3D case and to propose an unbiased evaluation metric to determine model quality. METHODS: Our approach minimizes a cost function based on the minimum description length (MDL) of the shape model to optimize landmark correspondences over the training set. In addition to the standard technique, we employ an extended remeshing method to change the landmark distribution without losing correspondences, thus ensuring a uniform distribution over all training samples. To break the dependency of the established evaluation measures generalization and specificity from the landmark distribution, we change the internal metric from landmark distance to volumetric overlap. RESULTS: Redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry. CONCLUSIONS: The distribution of landmarks on the training shapes is -- beyond the correspondence issue -- a crucial point in model construction.
Authors: Faezeh Fallah; Jürgen Machann; Petros Martirosian; Fabian Bamberg; Fritz Schick; Bin Yang Journal: MAGMA Date: 2016-09-16 Impact factor: 2.310