An, Woo Kyung (2016-08). Automated Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Mouse and Rat Brain Nissl Stained Data Sets. Master's Thesis. Thesis uri icon

abstract

  • The Knife-Edge Scanning Microscope (KESM), developed at the Brain Network Laboratory at Texas A&M University, can image a whole small animal brain at sub- micrometer resolution. Nissl data from the KESM enable us to look into vasculatures and cell bodies at the same time. Hence, analyzing the images from KESM mouse and rat Nissl data can help understand interactions between cerebral blood flow and its surrounding tissue. However, analysis is difficult since the image data contain complex cellular features, as well as imaging artifacts, which make it hard to extract the geometry of the vasculature and the cells. In this project, we propose a novel approach to reconstructing the neurovascular networks from whole-brain mouse and partial rat Nissl data sets. The proposed method consists of (1) pre-processing, (2) thresholding, and (3) post-processing. Initially, we enhanced the raw image data in the pre-processing step. Next, we applied a dynamic global thresholding to ex-tract vessels in the thresholding step. Subsequently, in the post-processing step, we computed local properties of the connected components to remove various sources of noise and we applied artificial neural networks to extract vasculatures. Concurrently, the proposed method connected small and large discontinuities in the vascular traces. To validate the performance of the proposed method, we compared reconstruction results of the proposed method with an alternative method (Lim's method). The comparison shows that the proposed method significantly outperforms (nine times faster, and more robust to noise) Lim's method. As a consequence, the proposed method provides a framework that can be applied to other data sets, even when the images contain a large portion of low-contrast images across the image stacks. We expect that the proposed method will contribute to studies investigating the correlation between the soma of the cells and microvascular networks.
  • The Knife-Edge Scanning Microscope (KESM), developed at the Brain Network
    Laboratory at Texas A&M University, can image a whole small animal brain at sub-
    micrometer resolution. Nissl data from the KESM enable us to look into vasculatures
    and cell bodies at the same time. Hence, analyzing the images from KESM mouse
    and rat Nissl data can help understand interactions between cerebral blood flow and its surrounding tissue. However, analysis is difficult since the image data contain
    complex cellular features, as well as imaging artifacts, which make it hard to extract
    the geometry of the vasculature and the cells.

    In this project, we propose a novel approach to reconstructing the neurovascular networks from whole-brain mouse and partial rat Nissl data sets. The proposed method consists of (1) pre-processing, (2) thresholding, and (3) post-processing. Initially, we enhanced the raw image data in the pre-processing step. Next, we applied a dynamic global thresholding to ex-tract vessels in the thresholding step. Subsequently, in the post-processing step, we computed local properties of the connected components to remove various sources of noise and we applied artificial neural networks to extract vasculatures. Concurrently, the proposed method connected small and large discontinuities in the vascular traces.

    To validate the performance of the proposed method, we compared reconstruction
    results of the proposed method with an alternative method (Lim's method). The
    comparison shows that the proposed method significantly outperforms (nine times
    faster, and more robust to noise) Lim's method. As a consequence, the proposed
    method provides a framework that can be applied to other data sets, even when
    the images contain a large portion of low-contrast images across the image stacks.
    We expect that the proposed method will contribute to studies investigating the
    correlation between the soma of the cells and microvascular networks.

publication date

  • August 2016