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.
This work was partly supported by the European Union FP7 Project KHRESMOI (FP7-257528), by the NSF IIS/CRCNS 0904625 grant, the NSF CAREER 0642971 grant, the NIH NCRR NAC P41-RR13218 and the NIH NIBIB NAMIC U54-EB005149 grant. Further supported by the Austrian National Bank grants COBAQUO (12537), BIOBONE (13468) and AORTAMOTION (13497).
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Donner, R., Birngruber, E., Steiner, H., Bischof, H., Langs, G. (2011). Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_9
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