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Abstract #2840

Random Forest based Calf Muscle Segmentation from MR data incorporating Prior Information

Marc Fischer1,2, Martin Schwartz1,2, Bin Yang2, and Fritz Schick1

1Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Delineation of muscle structures from MR images is an intricate but essential step for quantitative morphological assessment in many areas. In this work segmentation of muscles in the right calf from 2D MR data has been performed. Since challenging conditions prevail, prior information was incorporated in a Machine Learning driven approach. Versatile Random Forests were employed making use of annotated atlases as well as defined landmarks. It was demonstrated that incorporation of this prior information results in a feasible and fully automatic muscle segmentation.

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