D'Souza, Aswin Cletus (2007-12). Automated counting of cell bodies using Nissl stained cross-sectional images. Master's Thesis. Thesis uri icon

abstract

  • Cell count is an important metric in neurological research. The loss in numbers of certain cells like neurons has been found to accompany not only the deterioration of important brain functions but disorders like clinical depression as well. Since the manual counting of cell numbers is a near impossible task considering the sizes and numbers involved, an automated approach is the obvious alternative to arrive at the cell count. In this thesis, a software application is described that automatically segments, counts, and helps visualize the various cell bodies present in a sample mouse brain, by analyzing the images produced by the Knife-Edge Scanning Microscope (KESM) at the Brain Networks Laboratory. The process is described essentially in five stages: Image acquisition, Pre- Processing, Processing, Analysis and Refinement, and finally Visualization. Nissl staining is a staining mechanism that is used on the mouse brain sample to highlight the cell bodies of our interest present in the brain, namely neurons, granule cells and interneurons. This stained brain sample is embedded in solid plastic and imaged by the KESM, one section at a time. The volume that is digitized by this process is the data that is used for the purpose of segmentation. While most sections of the mouse brain tend to be comprised of sparsely populated neurons and red blood cells, certain sections near the cerebellum exhibit a very high density and population of smaller granule cells, which are hard to segment using simpler image segmentation techniques. The problem of the sparsely populated regions is tackled using a combination of connected component labeling and template matching, while the watershed algorithm is applied to the regions of very high density. Finally, the marching cubes algorithm is used to convert the volumetric data to a 3D polygonal representation. Barring a few initializations, the process goes ahead with minimal manual intervention. A graphical user interface is provided to the user to view the processed data in 2D or 3D. The interface offers the freedom of rotating and zooming in/out of the 3D model, as well as viewing only cells the user is interested in analyzing. The segmentation results achieved by our automated process are compared with those obtained by manual segmentation by an independent expert.
  • Cell count is an important metric in neurological research. The loss in numbers
    of certain cells like neurons has been found to accompany not only the deterioration of
    important brain functions but disorders like clinical depression as well. Since the manual
    counting of cell numbers is a near impossible task considering the sizes and numbers
    involved, an automated approach is the obvious alternative to arrive at the cell count. In
    this thesis, a software application is described that automatically segments, counts, and
    helps visualize the various cell bodies present in a sample mouse brain, by analyzing the
    images produced by the Knife-Edge Scanning Microscope (KESM) at the Brain
    Networks Laboratory.
    The process is described essentially in five stages: Image acquisition, Pre-
    Processing, Processing, Analysis and Refinement, and finally Visualization. Nissl
    staining is a staining mechanism that is used on the mouse brain sample to highlight the
    cell bodies of our interest present in the brain, namely neurons, granule cells and
    interneurons. This stained brain sample is embedded in solid plastic and imaged by the
    KESM, one section at a time. The volume that is digitized by this process is the data that
    is used for the purpose of segmentation.
    While most sections of the mouse brain tend to be comprised of sparsely
    populated neurons and red blood cells, certain sections near the cerebellum exhibit a
    very high density and population of smaller granule cells, which are hard to segment
    using simpler image segmentation techniques. The problem of the sparsely populated
    regions is tackled using a combination of connected component labeling and template matching, while the watershed algorithm is applied to the regions of very high density.
    Finally, the marching cubes algorithm is used to convert the volumetric data to a 3D
    polygonal representation.
    Barring a few initializations, the process goes ahead with minimal manual
    intervention. A graphical user interface is provided to the user to view the processed data
    in 2D or 3D. The interface offers the freedom of rotating and zooming in/out of the 3D
    model, as well as viewing only cells the user is interested in analyzing. The
    segmentation results achieved by our automated process are compared with those
    obtained by manual segmentation by an independent expert.

publication date

  • December 2007