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Image-Based Biomedical Big Data AnalyticsJens Rittscher Department of Engineering Science, Nuffield Department of Medicine, University of Oxford
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
Utility• Specific biological questions
require very specific experimental designs
• Systematic data collections are expensive and time consuming 2
/ICVGIP 2010 /
05/05/12
Zebrafish AtlasJ. Tu,M. Bello, A. Yekta, J. Rittscher
Zebrafish
Normal development Developmental Defects
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~1 mm
~3 mm
~4 mm
Normal
Treated
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The Zebrafish AtlasArea Measures
HIND BRAIN
HEAD
EAR NOTOCHORDMUSCLE
MUSCLEEYE
FIN
FIN
SWIM BLADDER
LIV
ER
HEA
RT
GITRACT
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
The Zebrafish Atlas
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Endpoint Measure
Head width IJ
Eye diameter GH
Notochord length BC
Tail length BD
Pericardial edema index (PEI)
EF
Body length AB
Abdominal width KL
Trunk length CD
Pericardial edema
G
H
EI C K
DJ F L B
A
Length Measures
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Target Discovery Institute
High-ThroughputScreening
ChemicalBiology
MedicalChemistry
Mass Sectrometry
Epigenetics
Quantitative Imaging
Imaging Strategy
Bio-Medical Imaging in Oxford
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CRUK Oxford Centre
TargetDiscover
yInstitute
Engineering
Science
Understand Disease
Improve Therapy
Drug Discovery
Clinical Image Data(CT, MRI, Pathology)Preclinical Research
(+ Microscopy)
High-Content ScreeningMass-Spectrometry
Computer VisionMedical Imaging
Example: Cancer Research
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 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)
-Omics dataon biological pathways in human disease-Target discovery & validation – HT data-Drug mechanism of action, novel lead compounds
-Novel disease related target candidates-Correlative studies in-dicating novel relevantbiological pathways
IterativeProcess
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
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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 AnalyticsJens Rittscher Department of Engineering Science, Nuffield Department of Medicine, University of Oxford