@inproceedings{dcc5e047331f4feaa34c896378b0f6f4,
title = "Anatomic-landmark detection using graphical context modelling",
abstract = "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.",
keywords = "Graphical model, anatomical landmark detection, context modelling, parameter learning",
author = "Lichao Wang and Vasileios Belagiannis and Carsten Marr and Fabian Theis and Yang, {Guang Zhong} and Nassir Navab",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7164114",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1304--1307",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
}