<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Isotropic Reconstruction of Neural Morphology\r\nfrom Large Non-Isotropic 3D Electron Microscopy"^^ . "Neuroscientists are increasingly convinced that it is necessary to reconstruct\r\nthe precise wiring and synaptic connectivity of biological nervous systems to\r\neventually decipher their function. The urge to reconstruct ever larger and more\r\ncomplete synaptic wiring diagrams of animal brains has created an entire new\r\nsubfield of neuroscience: Connectomics. The reconstruction of connectomes is\r\ndifficult because neurons are both large and small. They project across distances\r\nof many millimeters but each individual neurite can be as thin as a few tens of\r\nnanomaters. In order to reconstruct all neurites in densely packed neural tissues,\r\nit is necessary to image this tissue at nanometer resolution which, today, is only\r\npossible with 3D electron microscopy (3D-EM).\r\nOver the last decade, 3D-EM has become significantly more reliable than ever\r\nbefore. Today, it is possible to routinely image volumes of up to a cubic millimeter,\r\ncovering the entire brain of small model organisms such as that of the fruit fly\r\nDrosophila melanogaster. These volumes contain tens or hundreds of tera-voxels\r\nand cannot be analyzed manually. Efficient computational methods and tools\r\nare needed for all stages of connectome reconstruction: (1) assembling distortion\r\nand artifact free volumes from serial section EM, (2) precise automatic recon-\r\nstruction of neurons and synapses, and (3) efficient and user-friendly solutions\r\nfor visualization and interactive proofreading. In this dissertation, I present new\r\ncomputational methods and tools that I developed to address previously unsolved\r\nproblems covering all of the above mentioned aspects of EM connectomics.\r\nIn chapter 2, I present a new method to correct for planar and non-planar axial\r\ndistortion and to sort unordered section series. This method was instrumental for\r\nthe first ever acquisition of a complete brain of an adult Drosophila melanogaster\r\nimaged with 3D-EM.\r\nMachine learning, in particular deep learning, and the availability of public\r\ntraining and test data has had tremendous impact on the automatic reconstruction\r\nof neurons and synapses from 3D-EM. In chapter 3, I present a novel artificial\r\nneural network architecture that predicts neuron boundaries at quasi-isotropic\r\nresolution from non-isotropic 3D-EM. The goal is to create a high-quality over-\r\nsegmentation with large three-dimensional fragments for faster manual proof-\r\nreading.\r\nIn chapter 4, I present software libraries and tools that I developed to support\r\nthe processing, visualization, and analysis of large 3D-EM data and connectome\r\nreconstructions. Using this software, we generated the largest currently existing\r\ntraining and test data for connectome reconstruction from non-isotropic 3D-EM.\r\nI will particularly emphasize my flexible interactive proof-reading tool Paintera\r\nthat I built on top of the libraries and tools that I have developed over the last\r\nfour years."^^ . "2019" . . . . . . . "Philipp"^^ . "Hanslovsky"^^ . "Philipp Hanslovsky"^^ . . . . . . "Isotropic Reconstruction of Neural Morphology\r\nfrom Large Non-Isotropic 3D Electron Microscopy (PDF)"^^ . . . "dissertation.pdf"^^ . . . "Isotropic Reconstruction of Neural Morphology\r\nfrom Large Non-Isotropic 3D Electron Microscopy (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #27375 \n\nIsotropic Reconstruction of Neural Morphology \nfrom Large Non-Isotropic 3D Electron Microscopy\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . .