@PHDTHESIS{OReilly:2014:PhD-Thesis,
  author = {O'Reilly, Martin T.},
  title = {A model-based method for 3D reconstruction of cerebellar parallel
	fibres from high-resolution electron microscope images},
  school = {UCL (University College London)},
  year = {2014},
  month = jan,
  abstract = {In order to understand how the brain works, we need to understand
	how its neural circuits process information. Electron microscopy
	remains the only imaging technique capable of providing sufficient
	resolution to reconstruct the dense connectivity between all neurons
	in a circuit. Automated electron microscopy techniques are approaching
	the point where usefully large circuits might be successfully imaged,
	but the development of automated reconstruction techniques lags far
	behind. No fully-automated reconstruction technique currently produces
	acceptably accurate reconstructions, and semi-automated approaches
	currently require an extreme amount of manual effort. This reconstruction
	bottleneck places severe limits on the size of neural circuits that
	can be reconstructed. Improved automated reconstruction techniques
	are therefore highly desired and under active development. The human
	brain contains ~86 billion neurons and ~80% of these are located
	in the cerebellum. Of these cerebellar neurons, the vast majority
	are granule cells. The axons of these granule cells are called parallel
	fibres and tend to be oriented in approximately the same direction,
	making 2+1D reconstruction approaches feasible. In this work we focus
	on the problem of reconstructing these parallel fibres and make four
	main contributions: (1) a model-based algorithm for reconstructing
	2D parallel fibre cross-sections that achieves state of the art 2D
	reconstruction performance; (2) a fully-automated algorithm for reconstructing
	3D parallel fibres that achieves state of the art 3D reconstruction
	performance; (3) a semi-automated approach for reconstructing 3D
	parallel fibres that significantly improves reconstruction accuracy
	compared to our fully-automated approach while requiring ~40 times
	less labelling effort than a purely manual reconstruction; (4) a
	"gold standard" ground truth data set for the molecular layer of
	the mouse cerebellum that will provide a valuable reference for the
	development and benchmarking of reconstruction algorithms.},
  keywords = {neural circuit reconstruction; connectomics; basic image features;
	BIFs; electron microscopy;},
  url = {http://www.ucl.ac.uk/~ucbpmor/docs/OReilly-phd-thesis-2014.pdf}
}