title: A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs.
abstract: We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.
authors: University of Copenhagen, Copenhagen, Denmark. kerstenp@gmx.de
authors: Kersten
authors: K
authors: Petersen
authors: Melanie
authors: M
authors: Ganz
authors: Peter
authors: P
authors: Mysling
authors: Mads
authors: M
authors: Nielsen
authors: Lene
authors: L
authors: Lillemark
authors: Alessandro
authors: A
authors: Crimi
authors: Sami S
authors: SS
authors: Brandt
mesh_terms: D001012:Aorta, Abdominal / Q000000981:diagnostic imaging; D001499:Bayes Theorem; D002114:Calcinosis / Q000000981:diagnostic imaging; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008954:Models, Biological; D009010:Monte Carlo Method; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012307:Risk Factors