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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