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Title: A pipeline for automated assessment of cell location in 3D mouse brain image sets
Author: Niedworok, C. J.
ISNI:       0000 0004 7223 9715
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Mapping the neuronal connectivity of the mouse brain has long been hampered by the laborious and time-consuming process of slicing, staining and imaging the brain tissue. Recent developments in automated 3D fluorescence microscopy, such as serial two- photon tomography (STP) and light sheet fluorescence microscopy, now allow for automated rapid 3D imaging of a complete mouse brain at cellular resolution. In combination with transsynaptic viral tracers, this paves the way for high-throughput brain mapping studies, which could greatly advance our understanding of the function of the brain. Because transsynaptic tracers label synaptically connected cells, the analysis of these whole-brain scans requires detection of fluorescently labelled cells and anatomical segmentation of the data, which are very labour- and time-intensive manual tasks and prevent high-throughput analysis. This thesis presents and validates two software tools to automate anatomical segmentation and cell detection in serial two photon (STP) scans of the mouse brain. Automated mouse atlas propagation (aMAP) segments the scans into anatomical regions by matching a 3D reference atlas to the data using affine and free-form image registration. The fast automated cell counting tool (FACCT) then detects fluorescently labelled cells in the dataset with a novel approach of stepwise data reduction combined with object detection using artificial neuronal networks. The tools are optimised for large datasets and are capable of processing a 2.5TB STP scan in under two days. The performance of aMAP and FACCT is evaluated on STP scans from retrograde connectivity tracing experiments using rabies virus injections in the primary visual cortex.
Supervisor: Margrie, T. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available