Why the study?
Does an automatically constructed active shape model improve left ventricular segmentation accuracy in 3D ultrasound images in patients with left-ventricular asynchrony?
Does an automatically constructed active shape model improve left ventricular segmentation accuracy in 3D ultrasound images in patients with left-ventricular asynchrony?
An automatically constructed active shape model fusing CT data with artificial 3D ultrasound images provides a plausible method for left ventricular segmentation.
In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach.
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Butakoff et al. (2014) studied this question.
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