A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Peter Kersten Petersen, Melanie Ganz, Peter Mysling, Mads Nielsen, Lene Lillemark Erleben, Alessandro Crimi, Sami Sebastian Brandt

We present a fully automated framework for scoring a patients 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.

OriginalsprogEngelsk
TidsskriftI E E E Transactions on Medical Imaging
Vol/bind31
Udgave nummer3
Sider (fra-til)663-676
Antal sider14
ISSN0278-0062
DOI
StatusUdgivet - 2012

ID: 37546329