Segmentation Strategies for Connectomics.
Vazquez-Reina, Amelio.
2012
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Abstract: A human
brain is estimated to have roughly 100 billion neurons connected through more than 100
thousand miles of axons and hundreds of trillions of synaptic connections. The full
neural circuit within a brain is called its connectome, and understanding how it works
and enables cognition, consciousness, or intelligence are important open questions in
science. Recent developments in ... read morehigh-throughput electron microscopy imaging have enabled
biologists to visually inspect brain tissue at resolutions of a few nanometers per
voxel, enough to enable the analysis of neural circuits. However, the amount of data one
would need to annotate to identify and reconstruct even a small circuit makes manual
reconstruction efforts prohibitive. In this thesis, we explore several computational
strategies to facilitate the semi-automatic and automatic reconstruction of neurons from
3D stacks of connectomic images. We first propose Active Ribbons, a method based on
deformable models and level set methods for tracing individual neurites that is amenable
for interactive segmentation. We show that, unlike conventional level set methods,
Active Ribbons can reliably capture neural membranes on electron microscopy stacks. We
then explore statistical models for automatic segmentation. We study the connection
between the automatic segmentation of video and the reconstruction of connectomic
stacks, and introduce Multiple Hypothesis Video Segmentation (MHVS), a method for the
on-line segmentation of image sequences using long-term trajectories of 2D segments as
possible labels. We demonstrate the applicability of MHVS in videos with an unknown
number of objects and varying complexity. Building on the experience with MHVS, we
propose Segmentation Fusion, a method for automatic neuron reconstruction that does not
require the explicit discovery of labels a priori and that outperforms the
state-of-the-art in automatic neuron reconstruction. We finally discuss several scaling
strategies for distributed neuron reconstruction and show what we think are the largest
neuron reconstruction results ever obtained in
connectomics.
Thesis (Ph.D.)--Tufts University, 2012.
Submitted to the Dept. of Computer Science.
Advisors: Eric Miller, and Hanspeter Pfister.
Committee: Eric Miller, Hanspeter Pfister, Jeff Lichtman, Lenore Cowen, Remco Chang, and Kyongbum Lee.
Keyword: Computer science.read less - ID:
- r781wt425
- Component ID:
- tufts:21162
- To Cite:
- TARC Citation Guide EndNote
