Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Active shape models—their training and application
Computer Vision and Image Understanding
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic lumbar vertebral identification using surface-based registration
Computers and Biomedical Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Localized Priors for the Precise Segmentation of Individual Vertebras from CT Volume Data
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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
Proceedings of the international conference on Multimedia information retrieval
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Cost-Sensitive Machine Learning
Cost-Sensitive Machine Learning
Robust MR spine detection using hierarchical learning and local articulated model
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Precise segmentation and identification of thoracic vertebrae is important for many medical imaging applications though it remains challenging due to the vertebra's complex shape and varied neighboring structures. In this paper, a new method based on learned bone-structure 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. For the run-time testing, we design a new coarse-to-fine, two-stage segmentation strategy: subregions of a vertebra first deform 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 vertebrae shapes are guaranteed. This algorithm performs successfully with reliable mean point-to-surface errors 0.95+/-0.91mm on 40 volumes. Consequently a vertebra identification scheme is also proposed via mean surface mesh 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 [5].