Fast anatomical structure localization using top-down image patch regression

Authors:
René Donner;Bjoern H. Menze;Horst Bischof;Georg Langs
Affiliations:
Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University Vienna, Austria, Institute for Computer Graphics and Vision, Graz University of Technology, Austria;CSAIL, MIT, Cambridge, MA and Asclepios Project, INRIA Sophia-Antipolis, France, Computer Vision Laboratory, ETH Zurich, Switzerland;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University Vienna, Austria, CSAIL, MIT, Cambridge, MA
Venue:
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Year:
2012

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Abstract

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time.