| dc.description.abstract | Mapping the microvascular networks in the brain can lead to significant scientific and clinical
insights. The recent advances of high-throughput physical sectioning light microscopy have
greatly contributed to reducing the gap in neuroimaging between large-scale, low-resolution techniques
and small-scale, high-resolution methods. The Brain Networks Laboratory at Texas A&M
University developed a serial sectioning microscopy technique called the Knife-Edge Scanning
Microscopy (KESM) to section and image the entire mouse brain at submicrometer resolution.
The KESM can be used to obtain information about a small animal organ, such as a whole mouse
or rat brain, at submicrometer resolution of 0:6 μm 0:7 μm 1:0 μm voxel size. In our effort to
map the entire vascular network in the mouse brain, the Brain Networks Laboratory perfused the
mouse brain vessels with India ink, and used the KESM to image the prepared brain.
However, the image data size of the entire mouse brain from the KESM is about 1.5 TB,
and is not easy to handle or analyze. Moreover, the dataset contains unintended noise from the
serial sectioning process. Because of these difficulties, previous studies partially analyzed the
structure of the mouse brain by manually selecting a small, noise-free portion (volume size under
1000 1000 1000 voxel) in the dataset. In addition to the KESM dataset, there have been
studies for vessel reconstruction and analysis of the whole mouse brain at lower resolution or of
partial brain regions at submicrometer resolution. However, to the best of our knowledge, there has
been no study for vessel reconstruction and analysis of the whole mouse brain at submicrometer
resolution.
Mapping the microvascular networks in the brain can lead to significant scientific and clinical
insights. The recent advances of high-throughput physical sectioning light microscopy have
greatly contributed to reducing the gap in neuroimaging between large-scale, low-resolution techniques
and small-scale, high-resolution methods. The Brain Networks Laboratory at Texas A&M
University developed a serial sectioning microscopy technique called the Knife-Edge Scanning
Microscopy (KESM) to section and image the entire mouse brain at submicrometer resolution.
The KESM can be used to obtain information about a small animal organ, such as a whole mouse
or rat brain, at submicrometer resolution of 0:6 μm x 0:7 μm x 1:0 μm voxel size. In our effort to
map the entire vascular network in the mouse brain, the Brain Networks Laboratory perfused the
mouse brain vessels with India ink, and used the KESM to image the prepared brain.
However, the image data size of the entire mouse brain from the KESM is about 1.5 TB,
and is not easy to handle or analyze. Moreover, the dataset contains unintended noise from the
serial sectioning process. Because of these difficulties, previous studies partially analyzed the
structure of the mouse brain by manually selecting a small, noise-free portion (volume size under
1000 x 1000 x 1000 voxel) in the dataset. In addition to the KESM dataset, there have been
studies for vessel reconstruction and analysis of the whole mouse brain at lower resolution or of
partial brain regions at submicrometer resolution. However, to the best of our knowledge, there has
been no study for vessel reconstruction and analysis of the whole mouse brain at submicrometer
resolution.
In this dissertation, I will present my dataset, and computational algorithms I developed to
trace and analyze morphological properties of the whole mouse brain vascular network at submicrometer
resolution. Since the data is available across the entire brain in full detail (the smallest
capillaries can be observed in our data), it enables the comparison of regional differences in morphological
properties and provides the systematic cleaning to remove and consolidate erroneous
images automatically, which enables the full tracing and analysis of the whole KESM mouse brain
dataset with richer vasculature information. I expect this dissertation can provide rich insights to
brain and neuroscience researchers. | en |
