Crystallization Image Analysis on the World Community Grid

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Crystallization Image Analysis on the World
Community Grid

• Christian A. Cumbaa and Igor Jurisica
• Jurisica Lab, Division of Signaling Biology
• Ontario Cancer Institute,
• Toronto, Ontario

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Why automate classification of protein
crystallization trial images?

• Hauptman-Woodward has 65,000,000 images.
• They want 65,000,000 outcomes.

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Why automate classification of protein
crystallization trial images?

• Assist or replace human screening
• Speed the search phase in protein crystallization
• Improve throughput, consistency, objectivity
• Enables data mining and statistical optimization
  of the crystallization process

clear
precipitate
crystal
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Image classification
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Truth data

• 96 study
• 96 proteins X 1536 images hand-scored by 3
  experts
• Presence/absence of 7 independent outcomes
• NESG SGPP
• 15000 images
• Hand-scored by 1 expert, same scoring system
• 50 unanimously-scored images
• 10 most interesting compound categories

96-study
NESG (crystals)
SGPP (crystals)
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Feature set

• 12375 features computed per image
• A few basic statistics
• 50 microcrystal features
• Euler number features, two variations
• 11 Blur levels
• 11 Blur levels X 4 thresholds
• Image energy
• 11 blur levels

• 2925 Grey-Level Co-occurrence Matrix features
• 3 different grey-level quantizations
• 13 basic functions
• 25 sample distances
• 100 directions
• Computable from every point in the image
• Distilled to max range, max mean, min mean
• 9500 image-blob features
• Radon edge-detection

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Our image analysis problem

• Computing all 12,375 features takes gt5 hours for
  a single image
• We have 165,000 images in our training set
• Features must be evaluated for quality
• The best features (10s or low 100s) must be
  computed for the remaining 65,000,000 images
• Massive computing resources required!

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Image analysis on the World Community Grid

• http//www.worldcommunitygrid.org
• a global, distributed-computing platform for
  solving large scientific computing problems with
  human impact
• 377,627 volunteers contribute idle CPU time of
  960,346 devices.
• Our project Help Conquer Cancer
• launched November 2007.
• HCC has two goals
• To survey a wide tract of image-feature space and
  identify image analysis algorithms and parameters
  (features) that best determine crystallization
  outcome.
• To perform the necessary image analysis on
  Hauptman Woodwards archive of 65,000,000
  crystallization trial images.

fundraising slogan of the Ontario Cancer
Institute and its parent organization.
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Image analysis on the World Community Grid

• HCC has two phases
• Phase I calculate 12,375 features per image on
  high-priority images, including 165,441
  hand-scored images.
• November 2007-May 2008
• analysis on hand-scored images completed January
  2008
• Phase II calculate the best features from Phase
  I on the backlog of HWI images
• Grid members have contributed 8,919 CPU-years so
  far to HCC, an average of 55 CPU-years per day.

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Phase I feature assessment
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Measuring feature quality
feature entropy

• Treat as random variables
• Image class
• Feature value
• Measure the mutual information between them
  (unit bits)
• entropy(class) entropy(feature)
  entropy(class,feature)

class entropy
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Measuring feature quality
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Information density microcrystal counts
parameter space
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Information density GLCM maximum range parameter
space
Clear
Precipitate
Crystal
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Information density Radon-Sobel soft sum
parameter space
Clear
Precipitate
Crystal
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Information density Radon-Sobel blob metrics
(means) parameter space
Clear
Precipitate
Crystal
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Towards Phase II image classification
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Building classifiers

• handpicked 74 features from peaks in the clear,
  precipitate and other mutual information plots
• two classification schemes
• three-way clear, non-crystal precipitate, other
• ten-way clear, phase separation, phase
  precipitate, skin, phase crystal, precip,
  precip skin, precip crystal, crystal, garbage
• naïve Bayes model
• leave-one-out cross-validation

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Measuring classifier accuracy precision and
recall
crystals
recall
I think these are crystals
precision
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Three-class distribution
Clear 24.3
Precipitate AND NOT crystal 52.7
Other 23.0
Confusion matrix
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Recall precision
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10-class distribution
Clear 33.83
Phase separation 7.00
Phase separation precipitate 0.50
Skin 0.79
Phase separation crystal 2.32
Precipitate 34.25
Precipitate skin 4.95
Precipitate crystal 7.53
Crystal 8.34
Garbage 0.55
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Confusion matrix
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Recall precision
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Acknowledgements

• Hauptman-Woodward Medical Research Institute
• George DeTitta, Joe Luft, Eddie Snell, Mike
  Malkowski, Angela Lauricella, Max Thayer, Raymond
  Nagel, Steve Potter, and the 96-study reviewers.
• World Community Grid
• Bill Bovermann, Viktors Berstis, Jonathan D.
  Armstrong, Tedi Hahn, Kevin Reed, Keith J.
  Uplinger, Nels Wadycki
• IBM Deep Computing
• Jerry Heyman
• Jurisica Lab
• Richard Lu
• All crystallization images were generated at the
  High-Throughput Screening lab at The
  Hauptman-Woodward Institute.

• Funding from
• NIH U54 GM074899
• Genome Canada
• IBM
• NSERC
• (and earlier work from)
• NIH P50 GM62413
• NSERC
• CITO