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

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Standard

A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs. / Petersen, Peter Kersten; Ganz, Melanie; Mysling, Peter; Nielsen, Mads; Erleben, Lene Lillemark; Crimi, Alessandro; Brandt, Sami Sebastian.

I: I E E E Transactions on Medical Imaging, Bind 31, Nr. 3, 2012, s. 663-676.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Petersen, PK, Ganz, M, Mysling, P, Nielsen, M, Erleben, LL, Crimi, A & Brandt, SS 2012, 'A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs', I E E E Transactions on Medical Imaging, bind 31, nr. 3, s. 663-676. https://doi.org/10.1109/TMI.2011.2174646

APA

Petersen, P. K., Ganz, M., Mysling, P., Nielsen, M., Erleben, L. L., Crimi, A., & Brandt, S. S. (2012). A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs. I E E E Transactions on Medical Imaging, 31(3), 663-676. https://doi.org/10.1109/TMI.2011.2174646

Vancouver

Petersen PK, Ganz M, Mysling P, Nielsen M, Erleben LL, Crimi A o.a. A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs. I E E E Transactions on Medical Imaging. 2012;31(3):663-676. https://doi.org/10.1109/TMI.2011.2174646

Author

Petersen, Peter Kersten ; Ganz, Melanie ; Mysling, Peter ; Nielsen, Mads ; Erleben, Lene Lillemark ; Crimi, Alessandro ; Brandt, Sami Sebastian. / A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs. I: I E E E Transactions on Medical Imaging. 2012 ; Bind 31, Nr. 3. s. 663-676.

Bibtex

@article{e5f4910524504f78bf48a00fd297cfcc,
title = "A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs",
abstract = "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.",
author = "Petersen, {Peter Kersten} and Melanie Ganz and Peter Mysling and Mads Nielsen and Erleben, {Lene Lillemark} and Alessandro Crimi and Brandt, {Sami Sebastian}",
year = "2012",
doi = "10.1109/TMI.2011.2174646",
language = "English",
volume = "31",
pages = "663--676",
journal = "I E E E Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

RIS

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

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1109/TMI.2011.2174646

DO - 10.1109/TMI.2011.2174646

M3 - Journal article

VL - 31

SP - 663

EP - 676

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

SN - 0278-0062

IS - 3

ER -

ID: 37546329