Anatomic-landmark detection using graphical context modelling
Wang L, Marr C, Theis F, Yang GZ, Navab N, Belagiannis V (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: IEEE Computer Society
Book Volume: 2015-July
Pages Range: 1304-1307
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Brooklyn, NY, USA
ISBN: 9781479923748
DOI: 10.1109/ISBI.2015.7164114
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.
Authors with CRIS profile
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
How to cite
APA:
Wang, L., Marr, C., Theis, F., Yang, G.Z., Navab, N., & Belagiannis, V. (2015). Anatomic-landmark detection using graphical context modelling. In Proceedings - International Symposium on Biomedical Imaging (pp. 1304-1307). Brooklyn, NY, USA: IEEE Computer Society.
MLA:
Wang, Lichao, et al. "Anatomic-landmark detection using graphical context modelling." Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA IEEE Computer Society, 2015. 1304-1307.
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