Yang, Wenjie (2014-12). Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set. Master's Thesis. Thesis uri icon

abstract

  • The 3D reconstruction of neurovascular network plays an important role in understanding the functions of the blood vessels in different brain regions. Many techniques have been applied to acquire microscopic neurovascular data. The Knife-Edge Scanning Microscope (KESM) is a physical sectioning microscopy instrument developed by the Brain Network Lab in Texas A&M University which enables imaging of an entire mouse brain at sub-micrometer resolution. With the KESM image data, we can trace the neurovascular structure of the whole mouse brain. For the large neurovascular volume like the KESM data set, complicated tracing algorithm with template matching process is not fast enough. Also, KESM imaging might involve gaps and noise in data when acquiring the large volume of data. To solve these issues, a novel automated neurovascular tracing and data analysis method with less processing time and high accuracy is developed in this thesis. First, an automated seed point selection algorithm was described in my approach. The seed points on every outer boundary surface of the volume were selected as the start points of tracing. Second, a vector-based tracing method was developed to trace vascular network in 3D space. Third, the properties of the extracted vascular network were analyzed. Finally, the accuracy of the tracing method was evaluated using synthetic data. This approach is expected to help explore the entire vascular network of KESM automatically without human assistance.
  • The 3D reconstruction of neurovascular network plays an important role in understanding the functions of the blood vessels in different brain regions. Many techniques have been applied to acquire microscopic neurovascular data. The Knife-Edge Scanning Microscope (KESM) is a physical sectioning microscopy instrument developed by the Brain Network Lab in Texas A&M University which enables imaging of an entire mouse brain at sub-micrometer resolution. With the KESM image data, we can trace the neurovascular structure of the whole mouse brain. For the large neurovascular volume like the KESM data set, complicated tracing algorithm with template matching process is not fast enough. Also, KESM imaging might involve gaps and noise in data when acquiring the large volume of data. To solve these issues, a novel automated neurovascular tracing and data analysis method with less processing time and high accuracy is developed in this thesis.

    First, an automated seed point selection algorithm was described in my approach. The seed points on every outer boundary surface of the volume were selected as the start points of tracing. Second, a vector-based tracing method was developed to trace vascular network in 3D space. Third, the properties of the extracted vascular network were analyzed. Finally, the accuracy of the tracing method was evaluated using synthetic data. This approach is expected to help explore the entire vascular network of KESM automatically without human assistance.

publication date

  • December 2014