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Local in vivo environment (LivE) imaging in bone: 2D histology to 3D micro-CT image registration
We have developed a LivE imaging method to register 2D histological sections into the 3D bone volume obtained by micro-CT. The aim of this project is to streamline and automatize the registration of serial sections for high-throughput analysis of thousands of cells within their in vivo environment.
Keywords: bone remodeling, micro-CT imaging, image processing, image registration, histology
Load-induced bone remodeling is a multiscale process mediated through complex interactions between multiple cell types and their mechanical micro-environments. At the tissue level, the availability of in vivo micro-CT scanners provides the possibility to track bone remodeling at multiple time points within a single animal. At the molecular level, methods such as immunohistochemistry or in situ hybridization are commonly used to locally determine protein or gene transcript levels in histological tissue sections. However, methods are still lacking to link mechanical loading at the organ scale to bone remodeling and molecular responses at the cellular scale.
We therefore developed a so called Local in vivo Environment (LivE) imaging method to link the protein expression of single cells to the local remodeling and mechanical environment in sections of mouse bone tissue. Using in-house registration techniques using gradient based optimisation, endpoint 2D histological data can be registered into the 3D bone volume obtained by micro-CT. This approach, combined with immunohistochemistry allows for the protein expression of single cells to be linked to their local mechanical microenvironment in vivo and to the bone remodeling history. The major drawback of our approach, however, is the amount of manual work needed to register histology sections into the micro-CT data sets. For example, a very good initial guess is required for the method to work. The student will thus further develop existing algorithms in order to improve the registration of serial sections of mouse bone into 3D micro-CT images. First prototypes employing particle swarm optimization and genetic algorithms indicated promising results while the currently used gradient based method seems less suitable for this particular problem. Especially students with interest and experience in computational sciences and image processing are encouraged to apply.
Tasks: 80% computational, 20 % image processing
Load-induced bone remodeling is a multiscale process mediated through complex interactions between multiple cell types and their mechanical micro-environments. At the tissue level, the availability of in vivo micro-CT scanners provides the possibility to track bone remodeling at multiple time points within a single animal. At the molecular level, methods such as immunohistochemistry or in situ hybridization are commonly used to locally determine protein or gene transcript levels in histological tissue sections. However, methods are still lacking to link mechanical loading at the organ scale to bone remodeling and molecular responses at the cellular scale.
We therefore developed a so called Local in vivo Environment (LivE) imaging method to link the protein expression of single cells to the local remodeling and mechanical environment in sections of mouse bone tissue. Using in-house registration techniques using gradient based optimisation, endpoint 2D histological data can be registered into the 3D bone volume obtained by micro-CT. This approach, combined with immunohistochemistry allows for the protein expression of single cells to be linked to their local mechanical microenvironment in vivo and to the bone remodeling history. The major drawback of our approach, however, is the amount of manual work needed to register histology sections into the micro-CT data sets. For example, a very good initial guess is required for the method to work. The student will thus further develop existing algorithms in order to improve the registration of serial sections of mouse bone into 3D micro-CT images. First prototypes employing particle swarm optimization and genetic algorithms indicated promising results while the currently used gradient based method seems less suitable for this particular problem. Especially students with interest and experience in computational sciences and image processing are encouraged to apply. Tasks: 80% computational, 20 % image processing
The aim of this project is to improve and automatize the registration of 2D histological sections into 3D micro-CT images of mouse bone.
The aim of this project is to improve and automatize the registration of 2D histological sections into 3D micro-CT images of mouse bone.
Ariane Scheuren (arianesc@hest.ethz.ch) and Dr. Patrik Christen (patrik.christen@hest.ethz.ch).
Institute for Biomechanics, ETH Zürich, Professorship Ralph Müller
Ariane Scheuren (arianesc@hest.ethz.ch) and Dr. Patrik Christen (patrik.christen@hest.ethz.ch). Institute for Biomechanics, ETH Zürich, Professorship Ralph Müller