We present a fully automatic real-time algorithm for robust and accurate left ventricular segmentation in three-dimensional (3D) cardiac ultrasound. Segmentation is performed in a sequential state estimation fashion using an extended Kalman filter to recursively predict and update the parameters of a 3D Active Shape Model (ASM) in real-time. The ASM was trained by tracing the left ventricle in 31 patients, and provided a compact and physiological realistic shape space. The feasibility of the proposed algorithm was evaluated in 21 patients, and compared to manually verified segmentations from a custom-made semi-automatic segmentation algorithm. Successful segmentation was achieved in all cases. The limits of agreement (mean±1.96SD) for the point-to-surface distance were 2.2±1.1 mm. For volumes, the correlation coefficient was 0.95 and the limits of agreement were 3.4±20 ml. Real-time segmentation of 25 frames per second was achieved with a CPU load of 22%. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Hansegård, J., Orderud, F., & Rabben, S. I. (2007). Real-time active shape models for segmentation of 3D cardiac ultrasound. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4673 LNCS, pp. 157–164). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_20
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