Browsing by Subject "KESM"
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Item Acquisition and Mining of the Whole Mouse Brain Microstructure(2010-10-12) Kwon, Jae-RockCharting out the complete brain microstructure of a mammalian species is a grand challenge. Recent advances in serial sectioning microscopy such as the Knife- Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical sectioning technique, have the potential to finally address this challenge. Nevertheless, there still are several obstacles remaining to be overcome. First, many of these serial sectioning microscopy methods are still experimental and are not fully automated. Second, even when the full raw data have been obtained, morphological reconstruction, visualization/editing, statistics gathering, connectivity inference, and network analysis remain tough problems due to the unprecedented amounts of data. I designed a general data acquisition and analysis framework to overcome these challenges with a focus on data from the C57BL/6 mouse brain. Since there has been no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general software framework for automated data acquisition and computational analysis of the KESM data, and conducted two scientific case studies to discuss how the mouse brain microstructure from the KESM can be utilized. I expect the data, tools, and studies resulting from this dissertation research to greatly contribute to computational neuroanatomy and computational neuroscience.Item Automatic Seedpoint Selection and Tracing of Microstructures in the Knife-Edge Scanning Microscope Mouse Brain Data Set(2011-10-21) Kim, DongkunThe 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.Item Real-Time Image Error Detection in Knife-Edge Scanning Microscope(2014-09-23) Zhang, WencongResearch about the microstructure of the brain provides important information to help understand the functions of the brain. In order to investigate large volume, high-resolution data of mouse brains, researchers from Brain Network Lab (BNL) at Texas A&M University (TAMU) have been developing the Knife-Edge Scanning Microscope (KESM) in the past decade. The KESM can simultaneously section and image brain tissues at sub-micrometer resolution. However, malfunctions of the system can cause imaging errors, which make images fail to provide valid information. Moreover, malfunctions, especially due to obstructions (such as tissue fragments) in the light path of the system, result in continued cutting while the obstructions are present. Since KESM is generally not attended by a full-time human operator, this results in data loss. To solve the problem, I developed an image error detection method to automatically find imaging errors in real-time. The method can detect errors by analyzing newly acquired images, report results to human operators and even stop the KESM cutting process if necessary so that data loss is avoided. The basic idea of the method is to solve error detection problem through image change detection algorithm as the images acquired by KESM are well-registered and they do not change too much from one slice to the next when there is no error. As a result, the method can detect imaging errors with 86% accuracy (F1-score) and finish a detection routine within 2 seconds, which is sufficient to achieve real-time detection. By integrating the error detection program into the KESM control system, the method enhanced the robustness of the system and reduced data loss.Item Reducing Chatter in Knife-Edge Scanning Microscopy(2014-12-18) Shah, Raj SunilThe Knife-Edge Scanning Microscope (KESM) employs a novel form of physical sectioning microscopy: Imaging of tissue while sectioning. KESM was developed in the Brain Networks Lab (BNL) at Texas A&M University. The KESM has been used to section animal tissue embedded in a plastic block using a diamond knife. During each cut, the plastic block containing the tissue contacts the knife and that impact induces vibrations, known as knife chatter. These vibrations introduce noise in the image captured from the cut slice. This research is aimed at determining a metric to quantify knife chatter in the images acquired using the KESM, and to test if the use of a vibrating knife reduces knife chatter. Knife chatter appears as repeated parallel streaks in the images. A quantitative characterization of knife chatter is difficult since there is no regular pattern with which it appears. Having no regular pattern makes it very challenging to detect the chatter using automated programs. Observing the Fast Fourier Transforms of the images tells us that a narrow vertical band around the central vertical axis contains information exclusively about the chatter, while most of the information about the object in the image is outside the vertical band that represents the knife chatter. Using this information, we can quantitatively characterize knife chatter as a ratio of (1) the width of the region in the Fast Fourier Transform that corresponds to the knife chatter, and (2) the width of the region that corresponds to the object. Determining if the introduction of vibrations in the KESM diamond knife affects the amount of knife chatter present in the images was achieved by sectioning specimens of nissl-stained zebra-fish embryos embedded into araldite blocks, at different knife vibration frequencies. External sinusoidal wave vibrations were introduced in the KESM knife from a signal generator throughout the sectioning process. These electrical signals were converted to mechanical waves at the tip of the KESM knife blade. Performing such experiments at different oscillation frequencies enabled us to compare data using the metric described above. The results indicate that sectioning tissues with external vibrations does affect the amount of total data bandwidth taken up just by the chatter, and in some cases, reduces the relative width of the bandwidth taken up by the chatter.Item Semi-Automated Reconstruction of Vascular Networks in Knife-Edge Scanning Microscope Mouse Brain Data(2014-08-14) Dileepkumar, AnanthThe KnifeEdge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. The data from KESM can be used in the reconstruction of neuronal and vascular structures in the mouse brain. Tracing the vascular network of the brain and reconstructing the topology allows us to map the circulatory pathways inside the brain. Studying these cerebro-vascular networks is important to understand and measure the consumption and access to energy, oxygen and nutrients by different regions of the brain. Presently, there are both manual and automated methods to trace the vascular network from images of the brain. The manual methods are limited by the time consuming nature of the process and the extensive manual labor required. Today, vascular reconstruction techniques focus either on tracing vessels at the macro-level in a whole brain or tracing micro vessels in a small section of the brain. In this thesis, I attempt to develop a new, more targeted approach to semi-automatically trace a single blood vessel and its associated network of branches. In my approach, the user provides the algorithm with a single seed point of a vessel to start exploration and can guide the system towards specific sub-branches or sub-networks to explore. This new approach is expected to help quickly trace the vascular network of the brain as well as reduce the manual effort involved and save computing power by limiting the scope of the reconstruction to a smaller sub-network of blood vessels.