title: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model.
abstract: 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 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. 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.
authors.affiliation: CAD & Knowledge Solutions, Siemens Healthcare, Malvern, PA 19355, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Ma
authors.forename: Le
authors.initials: L
authors.lastname: Lu
authors.forename: Yiqiang
authors.initials: Y
authors.lastname: Zhan
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhou
authors.forename: Marcos
authors.initials: M
authors.lastname: Salganicoff
authors.forename: Arun
authors.initials: A
authors.lastname: Krishnan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*