Cardiac LV Segmentation Using a 3D Active Shape Model Driven by Fuzzy Inference

摘要

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 base 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%.