Prediction of the location of the lumbar aorta using the first four lumbar vertebrae as a predictor

TY - CONF

T1 - Prediction of the location of the lumbar aorta using the first four lumbar vertebrae as a predictor

AU - Conrad-Hansen, Lars A.

AU - Raundahl, Jakob

AU - Tankó, Lázló B.

AU - Nielsen, Mads

N1 - Serie: Medical Imaging 2004: Image Processing, 5370 Titel på proceedings: Proceedings of SPIE Sider: 1271-1281

PY - 2004

Y1 - 2004

N2 - This paper is one of the first steps towards the development of a mass-screening tool, well-suited for quantizing the extend of calcific deposits in the lumbar aorta, which should deliver reliable and easily reproducible data. The major problem is that non-calcified parts of the aorta are not visible on conventional x-ray images. We investigate whether or not it is possible to predict the location of the lumbar aorta, using the first four lumbar vertebrae as prior. We build a conditional probabilistic model from 90 manually annotated datasets. Using this model we made inferences on the position of the aortic walls given the position and shape of the four vertebrae.Of particular interest is the performance of the probabilistic model in comparison to the mean aortic shape. Due to the fact that our data set for this particular study only contained 90 hand-annotated images, we evaluated the model using the "leave-one-out" method. The resulting distance from the predicted to the actual aorta was then compared to the distance from the mean aorta to the actual aorta.The obtained results are encouraging; our conditional model provides results that are up to 38% better than the prediction using only the mean shape, and yields an overlap index of 0.89, whereas the mean shape only produces 0.83.

AB - This paper is one of the first steps towards the development of a mass-screening tool, well-suited for quantizing the extend of calcific deposits in the lumbar aorta, which should deliver reliable and easily reproducible data. The major problem is that non-calcified parts of the aorta are not visible on conventional x-ray images. We investigate whether or not it is possible to predict the location of the lumbar aorta, using the first four lumbar vertebrae as prior. We build a conditional probabilistic model from 90 manually annotated datasets. Using this model we made inferences on the position of the aortic walls given the position and shape of the four vertebrae.Of particular interest is the performance of the probabilistic model in comparison to the mean aortic shape. Due to the fact that our data set for this particular study only contained 90 hand-annotated images, we evaluated the model using the "leave-one-out" method. The resulting distance from the predicted to the actual aorta was then compared to the distance from the mean aorta to the actual aorta.The obtained results are encouraging; our conditional model provides results that are up to 38% better than the prediction using only the mean shape, and yields an overlap index of 0.89, whereas the mean shape only produces 0.83.

U2 - 10.1117/12.535281

DO - 10.1117/12.535281

M3 - Poster

T2 - SPIE Medical Imaging

Y2 - 29 November 2010

ER -