Active shape models—their training and application
Computer Vision and Image Understanding
Shape Constrained Deformable Models for 3D Medical Image Segmentation
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A model-based approach to the segmentation of nasal cavity and paranasal sinus boundaries
Proceedings of the 32nd DAGM conference on Pattern recognition
Local regression based statistical model fitting
Proceedings of the 32nd DAGM conference on Pattern recognition
A novel 3d partitioned active shape model for segmentation of brain MR images
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Artificial enlargement of a training set for statistical shape models: application to cardiac images
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
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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.