Interactive 3D reconstruction of the spine from radiographs using a statistical shape model and second-order cone programming.

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TypeArticle
Journal titleConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
ISSN1557-170X
Volume2011
Pages57265729; # of pages: 4
Subjectalgorithm; article; automated pattern recognition; computer assisted diagnosis; evaluation; human; image quality; information retrieval; methodology; radiography; reproducibility; scoliosis; sensitivity and specificity; spine; three dimensional imaging; validation study; Algorithms; Humans; Imaging, Three-Dimensional; Information Storage and Retrieval; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Scoliosis; Sensitivity and Specificity; Spine
AbstractThree-dimensional models of the spine are commonly used to diagnose, to treat, and to study spinal deformities. Creating these models is however time-consuming and, therefore, expensive. We propose in this paper a reconstruction method that finds the most likely 3D reconstruction given a maximal error bound on a limited set of landmark locations supplied by the user. This problem can be solved using second-order cone programming, leading to a globally convergent method that is considerably faster than currently available methods. A user can, with our current implementation, interactively modify the landmark locations and receive instantaneous feedback on the effect of those changes on the 3D reconstruction instead of blindly selecting landmarks. The proposed method was validated on a set of 53 patients who had adolescent idiopathic scoliosis using real and synthetic tests. Test results showed that the proposed method is considerably faster than currents methods (about forty times faster), is extremely flexible, and offers comparable accuracy.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology (IIT-ITI)
Peer reviewedYes
NPARC number21271417
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Record identifierbc55176b-3abb-48a2-8be9-90e4cb4a5539
Record created2014-03-24
Record modified2016-05-09
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