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

TY - JOUR

T1 - A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs

AU - Petersen, Peter Kersten

AU - Ganz, Melanie

AU - Mysling, Peter

AU - Nielsen, Mads

AU - Erleben, Lene Lillemark

AU - Crimi, Alessandro

AU - Brandt, Sami Sebastian

PY - 2012/3

Y1 - 2012/3

N2 - 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.

AB - 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.

U2 - 10.1109/TMI.2011.2174646

DO - 10.1109/TMI.2011.2174646

M3 - Journal article

SN - 0278-0062

VL - 31

SP - 663

EP - 676

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

IS - 3

ER -