Dileepkumar, Ananth (2014-08). Semi-Automated Reconstruction of Vascular Networks in Knife-Edge Scanning Microscope Mouse Brain Data. Master's Thesis. Thesis uri icon

abstract

  • The KnifeEdge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. The data from KESM can be used in the reconstruction of neuronal and vascular structures in the mouse brain. Tracing the vascular network of the brain and reconstructing the topology allows us to map the circulatory pathways inside the brain. Studying these cerebro-vascular networks is important to understand and measure the consumption and access to energy, oxygen and nutrients by different regions of the brain. Presently, there are both manual and automated methods to trace the vascular network from images of the brain. The manual methods are limited by the time consuming nature of the process and the extensive manual labor required. Today, vascular reconstruction techniques focus either on tracing vessels at the macro-level in a whole brain or tracing micro vessels in a small section of the brain. In this thesis, I attempt to develop a new, more targeted approach to semi-automatically trace a single blood vessel and its associated network of branches. In my approach, the user provides the algorithm with a single seed point of a vessel to start exploration and can guide the system towards specific sub-branches or sub-networks to explore. This new approach is expected to help quickly trace the vascular network of the brain as well as reduce the manual effort involved and save computing power by limiting the scope of the reconstruction to a smaller sub-network of blood vessels.
  • The KnifeEdge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. The data from KESM can be used in the reconstruction of neuronal and vascular structures in the mouse brain. Tracing the vascular network of the brain and reconstructing the topology allows us to map the circulatory pathways inside the brain. Studying these cerebro-vascular networks is important to understand and measure the consumption and access to energy, oxygen and nutrients by different regions of the brain. Presently, there are both manual and
    automated methods to trace the vascular network from images of the brain. The manual methods are limited by the time consuming nature of the process and the extensive manual labor required. Today, vascular reconstruction techniques focus either on tracing vessels at the macro-level in a whole brain or tracing micro vessels in a small section of the brain. In this thesis, I attempt to develop a new, more targeted approach to semi-automatically trace a single blood vessel and its associated network of branches.

    In my approach, the user provides the algorithm with a single seed point of a vessel to start exploration and can guide the system towards specific sub-branches or sub-networks to explore. This new approach is expected to help quickly trace the vascular network of the brain as well as reduce the manual effort involved and save computing power by limiting the scope of the reconstruction to a smaller sub-network of blood vessels.

publication date

  • August 2014