Transcript Developing Big Data Image Analytics for Advancing Drug

Image-Based Biomedical
Big Data Analytics
Jens Rittscher
Department of Engineering Science,
Nuffield Department of Medicine, University
of Oxford
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Cabability vs. Utility
Technical Capability
• Robotic imaging platforms are
capable of generating large data
sets
• New imaging processes produce
massive complex multi-channel
data sets
PE EnVision
Platereader
Labcyte
Echo
555
GE IN Cell 6000
Confocal High Content Imager
HRB
SteriStore D
Utility
• Specific biological questions
require very specific
experimental designs
• Systematic data collections
are expensive and time
consuming
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Zebrafish Atlas
/
ICVGIP 2010 /
05/05/12
J. Tu,M. Bello, A. Yekta, J. Rittscher
Zebrafish
Normal development
Developmental Defects
~1 mm
~3 mm
Normal
~4 mm
Treated
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The Zebrafish Atlas
Area Measures
HEAD
HIND BRAIN
EAR
EYE
SWIM
BLADDER
MUSCLE
NOTOCHORD
MUSCLE
FIN
FIN
GI
TRACT
Endpoint
Colored area
Head
Light pink mesh
Eye
Black
Ear
Blue mesh
Heart
Medium green mesh
Liver
Red mesh
Swim bladder
Cyan mesh
Gastrointestinal tract
Light green mesh
Upper muscle
Yellow mesh
Notochord
Grey mesh
Lower muscle (tail)
Magenta mesh
*Trunk
area = body – head – ear –
eye
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The Zebrafish Atlas
G
I
C
E
K
Length Measures
A
H
J
Pericardial edema
F
L
D
Endpoint
Measure
Head width
IJ
Eye diameter
GH
Notochord length
BC
Tail length
Pericardial edema index
(PEI)
Body length
BD
Abdominal width
KL
Trunk length
CD
B
EF
AB
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Target Discovery Institute
High-Throughput
Screening
Mass
Sectrometry
Chemical
Biology
Epigenetics
Medical
Chemistry
Quantitative
Imaging
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Imaging Strategy
Bio-Medical Imaging in Oxford
Example: Cancer Research
CRUK
Oxford
Centre
Target
Discovery
Institute
Clinical Image Data
(CT, MRI, Pathology)
Preclinical Research
(+ Microscopy)
High-Content Screening
Mass-Spectrometry
Improve
Therapy
Understand
Disease
Engineerin
g
Science
Computer Vision
Medical Imaging
Drug
Discovery
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Big Data Theme & TDI Interactions
Target Discovery Institute
Experimental Platforms:
• Phenotypic Screening
• (Target based) HTS
• Chemical Biology
• Mass Spectrometry
• Cell Biology
• Medicinal Chemistry
• Pharmacogenomics
Research Areas:
• Epigenetics in cancer,
immunity &
neurodegeneration
• Proteostasis & UPS
system
• Chemical biology of
epigenetic regulators
Big Data Institute
-Novel disease related
target candidates
-Correlative studies indicating novel relevant
biological pathways
Iterative
Process
-Omics data
on biological pathways in human disease
-Target discovery & validation – HT data
-Drug mechanism of action, novel lead
compounds
Novel target candidates for
human diseases
Computational Platforms:
• Biomedical data
analytics
• Modelling
Research Areas:
• Integrating human
genome sequencing &
clinical patient data
• Information from clinical
trials
• Identification of target
candidates for human
diseases (NGS, GWAS)
Computational Pathology
Relevance & Impact
Trend: Digitisation of histology slides
changes current clinical workflows
Opportunity: Automated analysis
provides a broad spectrum of
quantitative measurements
Our focus: Develop computational
framework to improve cancer
diagnosis, manage treatment, and
evaluate new therapies (e.g.
immunotherapy)
Cancer Immunotherapy
Strategy to use the immune system to
target tumours.
Celebrated as a turning point in cancer and
Science breakthrough of 2013
For the responding patients, this therapy
together with others have prolonged
patients survival for years rather than
months. However, only 50% of patients
respond.
Question: How can we understand which
patients will respond to therapy?
J Couzin-Frankel Science 2013;342:1432-1433
Quantitative Tissue Imaging
Challenge: Computational method
that effectively assist pathologists and
capture disease relevant information.
Important aspects:
• Detection of specific cell types
(e.g. lymphocytes, goblet cells)
• Assessment of structures such as
glands, ducts, and blood vessels
• Capturing the local tissue
architecture.
In summary: A visual vocabulary for
tissue analysis
Machine Learning
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Moving Ahead
•
•
•
•
Robust algorithms are one part of
the puzzle.
Build on robust algorithms to
develop “enterprise level
applications”
Enable pattern recognition and
mining across anatomical scales
Enable biologists to interact and
work with the data
J. Rittscher, Characterization of Biological Processes through Automated Image Analysis (Review),
Annual Review of Biomedical Engineering, 12, pages 315-344, August 2010
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Image-based Biomedical
Big Data Analytics
Jens Rittscher
Department of Engineering Science,
Nuffield Department of Medicine, University
of Oxford
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