3d reconstruction of anatomical structures from serial em images
Post on 19-Dec-2015
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3D Reconstruction of Anatomical Structures from Serial EM images
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Biological Motivation• Reconstruct 3D structures
from serial stack of EM images to understand– Distribution of cell type and
organelles.– Connectivity and
vasculature.• Requires
– Tracing of cell membranes, organelles, blood vessels, etc.
– Identification of structures for serial correspondence.
• Currently, reconstruct only a small fraction of volume (very few objects).– Time consuming (~20hours
per specimen).– Wealth of information in
surround structures not utilized.
Synapse Web, Kristen M. Harris, PI
http://synapses.clm.utexas.edu/
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Serial TEM Dataset• typical volume:
– 20-50 slices – 8 x 5 x 0.05 m per slice– 40 to 100 m3 volume– fly brain volume: 0.1mm3
• resolution: – xy: 2.6 nm/pixel
(350~400 pixels/ m) – z: 0.05 m ~20 pixels
apart• storage size:
– small volume: ~100MB– fly brain: 3.2x1014 pixels– compression of 4 would
result in 73 terabytes. – (source: Fiala, BU)
serial direction
Data from Synapse Web, Kristen M. Harris, PI
http://synapses.clm.utexas.edu/
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Challenges for Computer Vision• Segmenting objects
– EM images are inherently noisy.– Gaps in membrane.– Adjacent structures share weak
membrane boundary.– Organelles too small to use
common descriptors such as texture.
• Identification and correspondence– Structures can merge, split,
appear, or disappear (yellow arrow).
– z-axis structures (red) are easier to maintain correspondences than lateral structures (green).
– z resolution much lower than xy resolution (large changes serially).
– Automatic registration difficult (no ground truth)
• 3D reconstruction– good software available, but
getting to this step is the challenge.
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Preliminary 2D Segmentation
• Parametric snakes• Red-initial contour• Green-final contour• Highly sensitive to
initialization (bottom)
• Automatic initialization is a big challenge.
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Preliminary 2D Segmentation
• Geometric active contours.
• Provides topological flexibility.
• Less sensitive to initialization.
• Adjacent objects often merge (bottom).
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Preliminary 2D Segmentation
• Level set with elastic edge interaction*
• Zero level contour of v provides “good” initialization.
• Still many problems.
*Xiang et al. J. Comp. Phys. 2006
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=µπr ¢
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Preliminary 2D Segmentation
• Previous method produces binary masks of cross sections.
• Correspondences can be made based on distance and area of overlap.
• Inconsistencies occur often (green)
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Preliminary 3D Reconstruction
• Reconstructing “everything” at the same time produces confusing volume.
• Inconsistencies in segmentation and correspondence produce artifacts.
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Open Issues
• 2D Segmentation challenges– Automatic initialization.– Segmenting adjacent objects sharing weak
edges.– Noise.
• Cross section correspondence– Identifying objects (synapse, mitochondria,
etc.)– Tracking contours serially and detecting
merging/splitting events.– Automatic registration.
• Current Work: simultaneous segmentation and correspondence.