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A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data


Krauth, A; Blanc, R; Poveda, A; Jeanmonod, D; Morel, A; Székely, G (2009). A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data. NeuroImage, 49(3):2053-2062.

Abstract

Functional neurosurgery relies on robust localization of the subcortical target structures, which cannot be visualized directly with current clinically available in-vivo imaging techniques. Therefore, one has still to rely on an indirect approach, by transferring detailed histological maps onto the patient's individual brain images. In contrast to macroscopic MRI atlases, which often represent the average of a population, each stack of sections, which a stereotactic atlas provides, is based on a single specimen. In addition to this bias, the anatomy is displayed with a highly anisotropic resolution, leading to topological ambiguities and limiting the accuracy of geometric reconstruction. In this work we construct an unbiased, high-resolution three-dimensional atlas of the thalamic structures, representing the average of several stereotactically oriented histological maps. 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 reconstructing the mean model. Starting with a reconstruction from a single stack of sections, we iteratively register the current reference model onto the available data and reconstruct a refined mean three-dimensional model. The results show that integration of multiple stereotactic anatomical data to produce an unbiased, mean model of the thalamic nuclei and their subdivisions is feasible and that the integration reduces problems of atlas reconstruction inherent to histological stacks to a large extent.

Abstract

Functional neurosurgery relies on robust localization of the subcortical target structures, which cannot be visualized directly with current clinically available in-vivo imaging techniques. Therefore, one has still to rely on an indirect approach, by transferring detailed histological maps onto the patient's individual brain images. In contrast to macroscopic MRI atlases, which often represent the average of a population, each stack of sections, which a stereotactic atlas provides, is based on a single specimen. In addition to this bias, the anatomy is displayed with a highly anisotropic resolution, leading to topological ambiguities and limiting the accuracy of geometric reconstruction. In this work we construct an unbiased, high-resolution three-dimensional atlas of the thalamic structures, representing the average of several stereotactically oriented histological maps. 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 reconstructing the mean model. Starting with a reconstruction from a single stack of sections, we iteratively register the current reference model onto the available data and reconstruct a refined mean three-dimensional model. The results show that integration of multiple stereotactic anatomical data to produce an unbiased, mean model of the thalamic nuclei and their subdivisions is feasible and that the integration reduces problems of atlas reconstruction inherent to histological stacks to a large extent.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neurosurgery
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:October 2009
Deposited On:14 Dec 2009 09:38
Last Modified:06 Dec 2017 22:23
Publisher:Elsevier
ISSN:1053-8119
Publisher DOI:https://doi.org/10.1016/j.neuroimage.2009.10.042
PubMed ID:19853042

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