Browsing by Subject "Knife-Edge Scanning Microscope"
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Item Exploration, Registration, and Analysis of High-Throughput 3D Microscopy Data from the Knife-Edge Scanning Microscope(2014-04-25) Sung, ChulAdvances in high-throughput, high-volume microscopy techniques have enabled the acquisition of extremely detailed anatomical structures on human or animal organs. The Knife-Edge Scanning Microscope (KESM) is one of the first instruments to produce sub-micrometer resolution ( ~1 ?m^(3)) data from whole small animal brains. We successfully imaged, using the KESM, entire mouse brains stained with Golgi (neuronal morphology), India ink (vascular network), and Nissl (soma distribution). Our data sets fill the gap of most existing data sets which have only partial organ coverage or have orders of magnitude lower resolution. However, even though we have such unprecedented data sets, we still do not have a suitable informatics platform to visualize and quantitatively analyze the data sets. This dissertation is designed to address three key gaps: (1) due to the large volume (several tera voxels) and the multiscale nature, visualization alone is a huge challenge, let alone quantitative connectivity analysis; (2) the size of the uncompressed KESM data exceeds a few terabytes and to compare and combine with other data sets from different imaging modalities, the KESM data must be registered to a standard coordinate space; and (3) quantitative analysis that seeks to count every neuron in our massive, growing, and sparsely labeled data is a serious challenge. The goals of my dissertation are as follows: (1) develop an online neuro-informatics framework for efficient visualization and analysis of the multiscale KESM data sets, (2) develop a robust landmark-based 3D registration method for mapping the KESM Nissl-stained entire mouse data into the Waxholm Space (a canonical coordinate system for the mouse brain), and (3) develop a scalable, incremental learning algorithm for cell detection in high-resolution KESM Nissl data. For the web-based neuroinformatics framework, I prepared multi-scale data sets at different zoom levels from the original data sets. And then I extended Google Maps API to develop atlas features such as scale bars, panel browsing, and transparent overlay for 3D rendering. Next, I adapted the OpenLayers API, which is a free mapping and layering API supporting similar functionality as the Google Maps API. Furthermore, I prepared multi-scale data sets in vector-graphics to improve page loading time by reducing the file size. To better appreciate the full 3D morphology of the objects embedded in the data volumes, I developed a WebGL-based approach that complements the web-based framework for interactive viewing. For the registration work, I adapted and customized a stable 2D rigid deformation method to map our data sets to the Waxholm Space. For the analysis of neuronal distribution, I designed and implemented a scalable, effective quantitative analysis method using supervised learning. I utilized Principal Components Analysis (PCA) in a supervised manner and implemented the algorithm using MapReduce parallelization. I expect my frameworks to enable effective exploration and analysis of our KESM data sets. In addition, I expect my approaches to be broadly applicable to the analysis of other high-throughput medical imaging data.Item Knife-Edge Scanning Microscope Mouse Brain Atlas In Vector Graphics For Enhanced Performance(2013-07-17) Choi, JinhoThe microstructure of the brain at the cellular level provides crucial information for the understanding of the function of the brain. A large volume of high-resolution brain image data from 3D microscopy is an essential resource to study detailed microstructures of the brain. Accordingly, we have worked on obtaining high-resolution image data of entire mouse brains using the Knife-Edge Scanning Microscope (KESM). Furthermore, to disseminate these high-resolution whole mouse brain data sets to the neuroscience research community, we developed a web-based brain atlas, the KESM Brain Atlas (KESMBA). To visualize the data sets in 3D while using only a standard web browser, we employed distance attenuation and Google Maps API. The KESMBA is a powerful tool to analyze and share the KESM mouse brain data sets, but the image loading was slow because of the number of raster image (PNG) tiles and the file size. Moreover, since Google Maps API is governed by a commercial license, it does not provide enough flexibility for customization, extension, and mirroring. To solve these issues, we designed and developed a new KESM mouse brain atlas that uses a vector graphics format called Scalable Vector Graphics (SVG) instead of PNG, and OpenLayers API instead of Google Maps API. The SVG-based KESMBA using OpenLayers allows faster navigation and exploration of the KESM data, and more overlay of layers with the 4 times reduced file size compared to PNG tiles. Due to the reduced file size, the SVG-based KESMBA using OpenLayers is 2.45 times faster than the original atlas. By enhancing the performance, the users can more easily access the KESM data. We expect the SVG-based KESMBA to accelerate new discoveries in neuroscience.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.