Grid-enabled automatic construction of a two-chamber cardiac PDM from a large database of dynamic 3D shapes

Abstract

Point Distribution Modelling (PDM) is an efficient generative technique that can be used to incorporate statistical shape priors into image analysis methods like Active Shape Models (ASMs) or Active Appearance Models (AAMs). They are described by a set of landmarks usually manually pinpointed in a training set. Frangi et al. [1] have proposed an automatic auto-landmarking technique capable of dealing with multi-object arrangements. In this paper, we present an experimental extension of this previous work, validating the method provided. Our contributions can be summarized as follows: A two-chamber shape model of the heart is constructed from a large data-set comprising 90 subjects and considering 5 phases of the cardiac cycle. The computational demand of our technique is addressed using Grid computing. The results of our experiments suggest that the method presented in [1] as a proof-of-concept, can truly cope with the large inter-subject and inter-phase deformations present in clinical cardiac data sets including pathologies. The achieved accuracy in our validation is comparable to the former tests.

Statistical shape models for 3D medical image segmentation: A review

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Automatic Quantitative Analysis of Myocardial Wall Motion and Thickening from Long-and Short-Axis Cine MRI Studies

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Simulated 3D ultrasound LV cardiac images for active shape model training

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