1. 1 Image-Based Biomedical Big Data Analytics Jens Rittscher Department of Engineering Science, Nuffield Department of Medicine, University of Oxford

2. 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

3. / ICVGIP 2010 / 05/05/12 Zebrafish Atlas J. Tu,M. Bello, A. Yekta, J. Rittscher

4. Zebrafish Normal developmentDevelopmental Defects 4 ~1 mm ~3 mm ~4 mm Normal Treated

5. 5 The Zebrafish Atlas Area Measures HIND BRAIN HEAD EAR NOTOCHORD MUSCLE EYE FIN SWIM BLADDER LIVER HEART GI TRACT EndpointColored area HeadLight pink mesh EyeBlack EarBlue mesh HeartMedium green mesh LiverRed mesh Swim bladderCyan mesh Gastrointestinal tract Light green mesh Upper muscleYellow mesh NotochordGrey mesh Lower muscle (tail)Magenta mesh * Trunk area = body – head – ear – eye

6. The Zebrafish Atlas 6 EndpointMeasure Head widthIJ Eye diameterGH Notochord lengthBC Tail lengthBD Pericardial edema index (PEI) EF Body lengthAB Abdominal widthKL Trunk lengthCD Pericardial edema G H EICK D J F L B A Length Measures

7. 7 Target Discovery Institute High-Throughput Screening High-Throughput Screening Chemical Biology Medical Chemistry Mass Sectrometry Epigenetics Quantitative Imaging

8. Imaging Strategy

9. Bio-Medical Imaging in Oxford 9 CRUK Oxford Centre Target Discovery Institute Engineerin g Science Understand Disease Improve Therapy Drug Discovery Drug Discovery Clinical Image Data (CT, MRI, Pathology) Preclinical Research (+ Microscopy) Clinical Image Data (CT, MRI, Pathology) Preclinical Research (+ Microscopy) High-Content Screening Mass-Spectrometry High-Content Screening Mass-Spectrometry Computer Vision Medical Imaging Computer Vision Medical Imaging Example: Cancer Research

10. 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 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) 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 data on biological pathways in human disease -Target discovery & validation – HT data -Drug mechanism of action, novel lead compounds -Omics data on 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 relevant biological pathways Iterative Process

11. Computational Pathology

12. 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)

13. 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

14. 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

15. Machine Learning 15

16. 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 16 J. Rittscher, Characterization of Biological Processes through Automated Image Analysis (Review), Annual Review of Biomedical Engineering, 12, pages 315-344, August 2010

17. 1 Image-based Biomedical Big Data Analytics Jens Rittscher Department of Engineering Science, Nuffield Department of Medicine, University of Oxford