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Multi-Object Segmentation in Chest X-Ray Using Cascaded Regression Ferns

Part of the Informatik aktuell book series (INFORMAT)

Abstract

Active shape and appearance models that are commonly employed for fast, regularized organ segmentation have several limitations. Here, we adapt the explicit shape regression framework popularized for deformable face alignment to the simultaneous segmentation of lungs, heart and clavicles in X-ray scans. ESR uses data-driven feature learning and combines multiple non-linear regressors in a cascaded manner. We performed extensive experiments and devised appropriate feature ranges, a suitable data augmentation scheme and representative shapes for multi-initialization. With these extensions we obtained new stateof- the-art results for X-ray segmentation outperforming all previous approaches applied to the same dataset and approaching human observer variability with sub-second computation times.

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  • DOI: 10.1007/978-3-662-54345-0_57
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Correspondence to In Young Ha .

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Ha, I.Y., Wilms, M., Heinrich, M.P. (2017). Multi-Object Segmentation in Chest X-Ray Using Cascaded Regression Ferns. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_57

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  • DOI: https://doi.org/10.1007/978-3-662-54345-0_57

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-54344-3

  • Online ISBN: 978-3-662-54345-0

  • eBook Packages: Computer Science and Engineering (German Language)