Zusammenfassung
An essential prerequisite for comprehensive medical image analysis is the robust and fast detection of anatomical structures in the human body. To this point, machine learning techniques are most often applied to address this problem, exploiting large annotated image databases to estimate parametric models for anatomy appearance. However, the performance of these methods is generally limited, due to suboptimal and exhaustive search strategies applied on large volumetric image data, e.g., 3D-CT scans.
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Ghesu FC, Georgescu B, Grbic S, et al. Robust multi-scale anatomical landmark detection in incomplete 3D-CT data. Proc MICCAI. 2017;Part I:194–202.
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Ghesu, F.C., Georgescu, B., Grbic, S., Maier, A., Hornegger, J., Comaniciu, D. (2018). Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_24
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DOI: https://doi.org/10.1007/978-3-662-56537-7_24
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