Paper Info
Title
From Local to Global Random Regression Forests: Exploring Anatomical Landmark Localization.
Abstract
State of the art anatomical landmark localization algorithms pair local Random Forest (RF) detection with disambiguation of locally similar structures by including high level knowledge about relative landmark locations. In this work we pursue the question, how much high-level knowledge is needed in addition to a single landmark localization RF to implicitly model the global configuration of multiple, potentially ambiguous landmarks. We further propose a novel RF localization algorithm that distinguishes locally similar structures by automatically identifying them, exploring the back-projection of the response from accurate local RF predictions. In our experiments we show that this approach achieves competitive results in single and multi-landmark localization when applied to 2D hand radiographic and 3D teeth MRI data sets. Additionally, when combined with a simple Markov Random Field model, we are able to outperform state of the art methods.
Year
Venue
Field
2016
MICCAI
Computer vision,Data set,Pattern recognition,Regression,Computer science,Markov random field,Artificial intelligence,Landmark,Random forest
DocType
Citations 
PageRank 
Conference
5
0.41
References 
Authors
7
3
Name
Order
Citations
PageRank
Darko Stern111513.31
Thomas Ebner2264.27
Martin Urschler334723.94