Active shape models—their training and application
Computer Vision and Image Understanding
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Shape Constrained Deformable Models for 3D Medical Image Segmentation
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Localized maximum entropy shape modelling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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
A locally deformable statistical shape model
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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Fitting statistical models is a widely employed technique for the segmentation of medical images. While this approach gives impressive results for simple structures, shape models are often not flexible enough to accurately represent complex shapes. We present a fitting approach, which increases the model fitting accuracy without requiring a larger training data-set. Inspired by a local regression approach known from statistics, our method fits the full model to a neighborhood around each point of the domain. This increases the model's flexibility considerably without the need to introduce an artificial segmentation of the structure. By adapting the size of the neighborhood from small to large, we can smoothly interpolate between localized fits, which accurately map the data but are more prone to noise, and global fits, which are less flexible but constrained to valid shapes only. We applied our method for the segmentation of teeth from 3D cone-beam ct-scans. Our experiments confirm that our method consistently increases the precision of the segmentation result compared to a standard global fitting approach.