Automatic lumbar vertebral identification using surface-based registration
Computers and Biomedical Research
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
3-d graph cut segmentation with Riemannian metrics to avoid the shrinking problem
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Computer Vision and Image Understanding
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
Machine Vision and Applications
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Precise segmentation and identification of thoracic vertebrae is important for many medical imaging applications whereas it remains challenging due to vertebra's complex shape and varied neighboring structures. In this paper, a new method based on learned bonestructure edge detectors and a coarse-to-fine deformable surface model is proposed to segment and identify vertebrae in 3D CT thoracic images. In the training stage, a discriminative classifier for object-specific edge detection is trained using steerable features and statistical shape models for 12 thoracic vertebrae are also learned. In the run-time, we design a new coarse-to-fine, two-stage segmentation strategy: subregions of a vertebra first deforms together as a group; then vertebra mesh vertices in a smaller neighborhood move group-wise, to progressively drive the deformable model towards edge response maps by optimizing a probability cost function. In this manner, the smoothness and topology of vertebra's shapes are guaranteed. This algorithm performs successfully with reliable mean point-to-surface errors 0.95±0.91 mm on 40 volumes. Consequently a vertebra identification scheme is also proposed via mean surface meshes matching. We achieve a success rate of 73.1% using a single vertebra, and over 95% for 8 or more vertebra which is comparable or slightly better than state-of-the-art [1].