@inproceedings{285bfd9d927f427d9da4f2f61fdb8403,
title = "Cardiac LV segmentation using a 3D active shape model driven by fuzzy inference",
abstract = "Manual quantitative analysis of cardiac left ventricular function using multi-slice CT is labor intensive because of the large datasets. In previous work, an intrinsically three-dimensional segmentation method for cardiac CT images was presented based on a 3D Active Shape Model (3D-ASM). This model systematically overestimated left ventricular volume and underestimated blood pool volume, due to inaccurate estimation of candidate points during the model update steps. In this paper, we propose a novel ASM candidate point generation method based on a Fuzzy Inference System (FIS), which uses image patches as an input. Visual and quantitative evaluation of the results for 7 out of 9 patients shows substantial improvement for endocardial contours, while the resulting volume errors decrease considerably (blood pool: -39±29 cubic voxels in the previous model, -0.66±6.2 cubic voxels in the current). Standard deviation of the epicardial volume decreases by approximately 50%.",
author = "{Assen, van}, H.C. and M.G. Danilouchkine and F. Behloul and H.J. Lamb and {Geest, van der}, R.J. and J.H.C. Reiber and B.P.F. Lelieveldt",
year = "2003",
doi = "10.1007/978-3-540-39899-8_66",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "533--540",
editor = "R.E. Ellis and M. Peters",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 : 6th International Conference, Montr{\'e}al, Canada, November 15-18, 2003. Proceedings",
address = "Germany",
}