Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation

In this chapter, we present an automatic object detection and segmentation framework based on Marginal Space Learning (MSL), which integrates the components described in previous chapters into a complete segmentation system. In addition, simple and efficient methods based on mesh resampling are developed to establish mesh point correspondence, required to train a mean shape for shape initialization and build a statistical shape model for object boundary delineation. We use the four-chamber heart segmentation in cardiac Computed Tomography (CT) data as an example to illustrate the segmentation framework. Most of the technologies developed for heart chamber segmentation are generic, therefore can be applied directly or adapted easily to segment other anatomies in different imaging modalities.

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