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Selected Readings in Vision and Graphics
edited by Luc Van Gool, Gábor Székely, Markus Gross, Bernt Schiele

Volume 62






Axel Günther Krauth,

Modeling of Subcortical Anatomy
and Variability from Stereotactic
Anatomical Atlases.

2010. XVI, 110 pages. EUR 64,00.

ISBN 978-3-86628-330-5








Functional neurosurgery relies on robust localization of the basal ganglia and the thalamic nuclei. These subcortical structures, however, cannot be visualized directly with current clinically available in-vivo imaging techniques. Therefore, one has still to rely on an indirect targeting approach, by transferring detailed histological maps of a stereotactic anatomical atlas onto the patient's individual brain images. Each stack of sections, which a stereotactic anatomical atlas provides, is based on a single specimen. Thus, the current targeting approach does not use knowledge about the anatomical variability of the structures of interest. Statistical shape models have proven to be reliable method for capturing the anatomical variability exemplified in a set of training elements. Moreover, if the structures of interest are only partially visible in the image data, such a model allows to predict the complete shape of each structure. In this work, construction and application of such a model for the thalamic nuclei and the basal ganglia from several stacks of sections is described. A stack of sections is created through histological processing of a brain hemisphere. As a result of this technique, the anatomy is displayed in each stack with a highly anisotropic resolution, leading to topological ambiguities and limiting the accuracy of geometric reconstruction. We resolve the topological ambiguity by combining the information provided by histological data from different stereotactic directions. Since the stacks differ not only in geometrical detail provided, but also due to inter- individual variability, we adopt an iterative approach for constructing the statistical shape model. Finally, the application of the developed model for segmentation purposes is explored. The first proposition depends on the registration of the mean of the shape model to a template MRI. The second approach relies on feature-based shape matching and the identification of correspondences between the model and prominent features in the MRI..


Axel Krauth studied Computer Science at the University of Passau, Germany, from which he obtained a Dipl.-Inf. Univ. degree in 2004. He performed his diploma thesis at the Surgical Planning Laboratory, Harvard Medical School, Boston, USA in the field of medical image processing. From 2005 to 2009, he was research assistant and Ph.D. student at the Computer Vision Laboratory of the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, with Prof. Dr. Gábor Székely. In 2009, he finished his doctoral thesis on "Modeling of subcortical anatomy and variability from stereotactic anatomical atlases" and was awarded a Ph.D. degree (Doctor of Sciences) from the ETH Zurich.

Keywords: Neuroanatomy, Stereotactic Anatomical Atlas, Medical Image Processing, 3D Reconstruction, Registration, Statistical Modeling

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