Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

L. Wang, V. Belagiannis, C. Marr, F. Theis, G. Z. Yang, N. Navab
Anatomic-Landmark Detection Using Graphical Context Modelling
Proceedings of 2015 IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, New York, USA, April 2015 (bib)

Anatomical landmarks in images play an important role in medical practice. This paper presents a graphical model that fully automatically detects such landmarks. The model includes a unary potential using a random forest classifier based on local appearance and binary and ternary potentials encoding geometrical context among different landmarks. The weightings of different potentials are learned in a maximum likelihood manner. The final detection result is formulated as the maximum-a-posteriori estimation jointly over the whole set of landmarks in one image. For validation, the model is applied to detect right-ventricle insert points in cardiac MR images. The result shows that the context modelling is able to substantially improve the overall accuracy.
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