Localization of 3D anatomical structures using random forests and discrete optimization

  • Authors:

  • René Donner;Erich Birngruber;Helmut Steiner;Horst Bischof;Georg Langs

  • Affiliations:

  • Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna and Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA

  • Venue:
  • MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
  • Year:
  • 2010

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Abstract

In this paper we propose a method for the automatic localization of complex anatomical structures using interest points derived from Random Forests and matching based on discrete optimization. During training landmarks are annotated in a set of example volumes. A sparse elastic model encodes the geometric constraints of the landmarks. A Random Forest classifier learns the local appearance around the landmarks based on Haar-like 3D descriptors. During search we classify all voxels in the query volume. This yields probabilities for each voxel that indicate its correspondence with the landmarks. Mean-shift clustering obtains a subset of 3D interest points at the locations with the highest similarity in a local neighboorhood. We encode these points together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field. By solving the discrete optimization problem the most probable locations for each model landmark are found in the query volume. On a set of 8 hand CTs we show that this approach is able to consistently localize the model landmarks (finger tips, joints, etc) despite the complex and repetitive structure of the object.