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%.
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van Assen, H.C. et al. (2003). Cardiac LV Segmentation Using a 3D Active Shape Model Driven by Fuzzy Inference. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39899-8_66
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DOI: https://doi.org/10.1007/978-3-540-39899-8_66
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