Active shape models—their training and application
Computer Vision and Image Understanding
Injectivity conditions of 2D and 3D uniform cubic B-spline functions
Graphical Models - Pacific Graphics '99 in Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
Diffeomorphic registration using b-splines
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient population registration of 3d data
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Graphical Models and Deformable Diffeomorphic Population Registration Using Global and Local Metrics
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
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Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-to-case segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.