Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set

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

Knife Edge Scanning Microscope Brain Atlas Interface for Tracing and Analysis of Vasculature Data

The study of the neurovascular network in the brain is important to understand brain functions as well as causes of several brain dysfunctions. Many techniques have been applied to acquire neurovascular data. The Knife-Edge Scanning Microscope (KESM), developed by the Brain Network Lab at Texas A&M University, can generate whole-brain-scale data at submicrometer resolution. The specimen can be stained with different stains, and depending on the type of stain used, the KESM can image different types of microstructures in the brain. The India ink stain allows the neurovascular network in the brain to be imaged. In order to visualize and analyze such large datasets (~ 1.5 TB per brain), a lightweight, web-based mouse brain atlas called the Knife-Edge Scanning Microscope Brain Atlas (KESMBA) was developed in the lab. The atlas serves several whole mouse brain data sets including India ink. The multi-section overlay technique used in the atlas enables 3D visualization of the structural information in the data. To solve the challenging issue of tracing micro-vessels in the brain, in this thesis a semi-automated tracing and analysis method is developed and integrated into the KESM brain atlas. Using the KESMBA interface developed in this thesis, the user can look at the 3D structure of the vessels on the brain atlas and can guide the tracing algorithm. To analyze the vasculature network traced by the user, a data analysis component is also added. This new KESMBA interface is expected to help in quickly tracing and analyzing the vascular network of the brain with minimal manual effort. In order to visualize and analyze such large data sets (~ 1.5 TB per brain), a light-weight, web-based mouse brain atlas called the Knife-Edge Scanning Microscope Brain Atlas (KESMBA) was developed in the lab. The atlas serves several whole mouse brain data sets including India ink. The multi-section overlay technique used in the atlas enables 3D visualization of the structural information in the data. To solve the challenging issue of tracing micro-vessels in the brain, in this thesis a semi-automated tracing and analysis method is developed and integrated into the KESM brain atlas. Using the KESMBA interface developed in this thesis, the user can look at the 3D structure of the vessels on the brain atlas and can guide the tracing algorithm. In order to analyze the vasculature network traced by the user, a data analysis component is also added. This new KESMBA interface is expected to help in quickly tracing and analyzing the vascular network of the brain with minimal manual effort

Automated Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Mouse and Rat Brain Nissl Stained Data Sets

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

Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set

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

Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope Rat Nissl Data Set Using a Computing Cluster

3D reconstruction of the neurovascular networks in the brain is a first step toward the analysis of their function. However, existing three dimensional imaging techniques have not been able to image tissues on a large scale at a high resolution in all three dimensions. For creating high-resolution neurovascular models, the Knife-Edge Scanning Microscope (KESM) at Texas A&M University has been developed and used to image whole rat brain vascular networks at submicrometer resolution. In this thesis, I describe algorithms that are fully automatic and compatible with the large KESM rat Nissl data set. The method consists of image enhancement, binarization, 3D neurovascular networks tracing, and quantizing anatomical statistics. These methods are easily parallelizable and are compatible with high-throughput microscopy data. A computing cluster has been used to increase the throughput of the methods. Using the method developed, I analyzed a large volume of rat brain vasculature data. The results are expected to shed light on the structural organization of the vascular network that underlies the delivery of oxygen, nutrients, and signaling molecules throughout the brain

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions

Imaging and Computational Methods for Exploring Sub-cellular Anatomy

The ability to create large-scale high-resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function. This is particularly important for brain tissue, where the majority of function occurs at the cellular and sub-cellular level. However, reconstructing tissue at sub-cellular resolution is a complex problem that requires new methods for imaging and data analysis. In this dissertation, I describe a prototype microscopy technique that can image large volumes of tissue at sub-cellular resolution. This method, known as Knife-Edge Scanning Microscopy (KESM), has an extremely high data rate and can capture large tissue samples in a reasonable time frame. We can therefore image complete systems of cells, such as whole small animal organs, in a matter of days. I then describe algorithms that I have developed to cope with large and complex data sets. These include methods for improving image quality, tracing filament networks, and constructing high-resolution anatomical models. These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high-throughput microscopy data. The resulting models of large-scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques

Automatic Seedpoint Selection and Tracing of Microstructures in the Knife-Edge Scanning Microscope Mouse Brain Data Set

The Knife-Edge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. By using the data sets from the KESM, we can trace the neuronal and vascular structures of the whole mouse brain. I investigated effective methods for automatic seedpoint selection on 3D data sets from the KESM. Furthermore, based on the detected seedpoints, I counted the total number of somata and traced the neuronal structures in the KESM data sets. In the first step, the acquired images from KESM were preprocessed as follows: inverting, noise filtering and contrast enhancement, merging, and stacking to create 3D volumes. Second, I used a morphological object detection algorithm to select seedpoints in the complex neuronal structures. Third, I used an interactive 3D seedpoint validation and a multi-scale approach to identify incorrectly detected somata due to the dense overlapping structures. Fourth, I counted the number of somata to investigate regional differences and morphological features of the mouse brain. Finally, I traced the neuronal structures using a local maximum intensity projection method that employs moving windows. The contributions of this work include reducing time required for setting seedpoints, decreasing the number of falsely detected somata, and improving 3D neuronal reconstruction and analysis performance

Skeletonization-Based Automated Tracing and Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Mouse Brain India Ink Dataset

The vascular architecture of the brain is very complex and reconstruction of this architecture can aid in understanding the functions of vessels in different regions of the brain. Advances in microscopy have enabled high-throughput imaging of massive volumes of biological microstructure at a very high resolution. The Knife Edge Scanning Microscope (KESM), developed by the Brain Network Laboratory at Texas A & M University, is one such instrument that enables imaging of whole small animal brains at sub-micrometer resolution. The KESM has been successfully used to acquire vasculature dataset from a mouse brain stained by India ink. However, manual tracing and reconstruction of vessels is not feasible due to the huge volume of the dataset. Therefore, developing efficient and robust automatic tracing methods is essential for analysis of the network. This thesis presents an efficient skeletonization based tracing algorithm to reconstruct the vascular structure in the KESM India ink data set. The skeleton is generated from the original volume by repetitively removing voxels from the object’s boundary such that the connectivity and topology in the original volume is preserved. The skeleton is then dilated to reconstruct the volume. The accuracy of this method is determined by comparing the reconstructed volume with the original volume. This method is expected to trace the entire vascular network of the brain quickly and with high accuracy without human assistance