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Description:
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This dissertation studies two problems related to geometric representation of
neuroanatomical data : (i ) spatial representation and organization of individual neurons ,
and (ii ) reconstruction of three -dimensional neuroanatomical regions from sparse two -dimensional
drawings . This work has been motivated by nearby development of new
technology , Knife -Edge Scanning Microscopy (KESM ) , that images a whole mouse
brain at cellular level in less than a month .
A method is introduced to represent neuronal data observed in the mammalian brain at
the cellular level using geometric primitives and spatial indexing . A data representation
scheme is defined that captures the geometry of individual neurons using traditional
geometric primitives , points and cross -sectional areas along a trajectory . This
representation captures inferred synapses as directed links between primitives and
spatially indexes observed neurons based on the locations of their cell bodies . This
method provides a set of rules for acquisition , representation , and indexing of KESMgenerated
data .
Neuroanatomical data observed at the gross level provides the underlying regional
framework for neuronal circuits . Accumulated expert knowledge on neuroanatomical organization is usually given as a series of sparse two -dimensional contours . A data
structure and an algorithm are described to reconstruct separating surfaces among
multiple regions from these sparse cross -sectional contours . A topology graph is defined
for each region that describes the topological skeleton of the region’s boundary surface
and that shows between which contours the surface patches should be generated . A
graph -directed triangulation algorithm is provided to reconstruct surface patches
between contours . This graph -directed triangulation algorithm combined together with
a piecewise parametric curve fitting technique ensures that abutting or shared surface
patches are precisely coincident . This method overcomes limitations in i ) traditional
surfaces -from -contours algorithms that assume binary , not multiple , regionalization of
space , and in ii ) few existing separating surfaces algorithms that assume conversion of
input into a regular volumetric grid , which is not possible with sparse inter -planar
resolution . |