Organ Recognition In medical collaboration with: Axel Möller Martinez; Stephan Nekolla; Nuklearmedizin, Klinikum Rechts der Isar Scientific Director: Nassir Navab Contact Person(s): Olivier Pauly In industrial collaboration with: Ben Glocker; Antonio Criminisi (Microsoft Research Cambridge)

Keywords: Machine Learning for Medical Applications

Abstract

Automatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. In this project, we investigate hierachical regression methods based on Random Forests and Random Ferns. Such hierarchical approaches permit to subdivide efficiently the feature space and to create a partition over it. In each cell of the resulting partition, data can be easily modeled using simple mathematical models such as constant or linear. The combination of these models over the whole partition results then in a complex non-linear model.

Pictures

Figure 1: Regression Forests: toy example

Publications

2011
	O. Pauly, B. Glocker, A. Criminisi, D. Mateus, A. Martinez-Möller, S. Nekolla, N. Navab Fast Multiple Organs Detection and Localization in Whole-Body MR Dixon Sequences To appear in Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, September 2011 (bib)

Team

Contact Person(s)

Dr. Olivier Pauly

Working Group

Prof. Diana Mateus

Prof. Dr. Nassir Navab

Dr. Olivier Pauly

Location

Technische Universität München Institut für Informatik / I16 Boltzmannstr. 3 85748 Garching bei München Tel.: +49 89 289-17058 Fax: +49 89 289-17059

Visit our lab at Garching.



internal project page

Please contact Olivier Pauly for available student projects within this research project.