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Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation

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Marginal Space Learning for Medical Image Analysis

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Abstract

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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Authors and Affiliations

  1. Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA

    Yefeng Zheng & Dorin Comaniciu

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  1. Yefeng Zheng
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  2. Dorin Comaniciu
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Zheng, Y., Comaniciu, D. (2014). Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation. In: Marginal Space Learning for Medical Image Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0600-0_7

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  • DOI: https://doi.org/10.1007/978-1-4939-0600-0_7

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