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CORBEL Bioimage Analysis webinar slides

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The CORBEL webinar series continues with a basic introduction to bioimage analysis by Christian Tischer, from the EMBL Centre for Bioimage Analysis (CBA). During this webinar, Christian Tischer will explain basic concepts of (automated) bioimage analysis. The webinar includes a short overview of what bioimage analysis actually is and why it is useful. Subsequently, image formation, restoration, filtering and segmentation will be addressed, as well as object measurements and image analysis quality control will be discussed.

CORBEL (http://www.corbel-project.eu) is an initiative of eleven new biological and medical research infrastructures (BMS RIs), which together will create a platform for harmonised user access to biological and medical technologies, biological samples and data services required by cutting-edge biomedical research. CORBEL will boost the efficiency, productivity and impact of European biomedical research.

This webinar took place on 11th April 2019 and is part of the CORBEL webinar series. A recording of the webinar is available through the CORBEL website:
https://www.corbel-project.eu/webinars/bbmri-eric-quality-management-services.html

For previous and upcoming CORBEL webinars see:
http://www.corbel-project.eu/webinars

Publicada em: Ciências
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CORBEL Bioimage Analysis webinar slides

  1. 1. A Basic Introduction to Bioimage Analysis Presenters: ChristianTischer (Centre for Bioimage Analysis, EMBL) Host: Michelle Mendonca (EMBL-EBI) 11/04/2019 1 CORBELWebinar Series
  2. 2. 11/04/2019 2 This webinar is being recorded
  3. 3. AUDIENCE Q&A SESSION 11/04/2019 3 Please write your questions in the questions window of the GoToWebinar application
  4. 4. BACKGROUND 4 Since 2015, thirteen ESFRI Research Infrastructures from the field of BioMedical Science (BMS RI) joined their scientific capabilities and services to transform the understanding of biological mechanisms and accelerate its translation into medical care. • biobanking & biomolecular resources • curated databases • marine model organisms • systems biology • translational research • functional genomics • screening & medicinal chemistry • microorganisms • clinical trials • structural biology • biological/medical imaging• plant phenotyping • highly pathogenic microorganisms
  5. 5. CORBEL MISSION 5 Modern biological and biomedical research involves complex projects and a variety of different technologies. Some of the most important discoveries are made at the interface between different disciplines. CORBEL will harmonise access and services for complex research projects involving more than one RI that offer: • biological and medical technologies • biological samples and • data services
  6. 6. TODAY’S PRESENTER 11/04/2019 6 ChristianTischer studied physics in Heidelberg, followed by PhD at EMBL with Philippe Bastiaens working on microscope development and signalling in mammalian cells. Subsequently, he did a postdoc with Marileen Dogertom at AMOLF in the Netherlands working on microtubule dynamics. From 2009 until 2018, he worked at EMBL's Advanced Light Microscopy Facility (ALMF) supporting scientists with both microscopy and image analysis. Since end of 2018, Christian’s focus shifted completely towards image analysis support and he is now running EMBL’s Centre for Bioimage Analysis (CBA).
  7. 7. Bioimage Analysis Christian Tischer Centre for Bioimage Analysis (CBA) European Molecular Biology Laboratory (EMBL)
  8. 8. Bioimage Analysis Automatically extract biophysically meaningful information from microscopy images of biological samples. Bioimage Analyst EU funded action: https://www.cost.eu/actions/CA15124 Network of European Bioimage Analysts: www.neubias.org
  9. 9. Quantification, Reproducibility & Statistics Control Treated **
  10. 10. Image = { Pixels } x y Pixel = ( value, coordinate )
  11. 11. Image Data Type: Pixel Value Range Pixel = ( value, coordinate ) ● unsigned byte (8-bit): 0,1, .., 255 (2^8 – 1) ○ 35 – 50 = 0; 250 + 10.2 = 255 ● unsigned short (16-bit): 0,1, .., 65535 (2^16 – 1) ○ 35 – 50 = 0; 250 + 10.2 = 260 ● float (32-bit): -3.4028235 × 1038, …, -3.4028235 × 1038 ○ 35.0 – 50.0 = -15.0; 250 + 10.2 = 260.2 ○ 1000000000 + 1000 = 1000001024
  12. 12. Image Data Type Conversions ● Lower bit depth => Higher bit depth ○ e.g. 8-bit to 16-bit ○ usually preserves pixel values ● Higher bit depth => Lower bit depth ○ e.g. 16-bit to 8-bit ○ often deteriorates pixels values
  13. 13. Image Formation: Blur and Noise
  14. 14. Image Restoration: Counteracting Image Formation Formation: Noise, Blur Restoration: Denoising, Deblurring Weigert M (2018) Nat Methods 15(12):1090-1097
  15. 15. Image Processing: Convolution Filters average +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 52.857 53.619 51.286 52.714 54.571 53.238 53.000 54.143 53.095 52.381 51.810 52.286 52.952 53.810 53.286 54.714 54.714 54.286 54.429 53.238 54.810 55.476 53.952 53.810 54.810
  16. 16. Image Processing: Convolution Filters edge +1 0 -1 +1 0 -1 +1 0 -1 + 89 + 86 + 93 - 46 - 57 - 49 = 116
  17. 17. Image Segmentation segment Gray values Objects
  18. 18. Image Segmentation: Example Workflow 1 2 threshold connected component analysis
  19. 19. Image Processing Facilitates Image Segmentation threshold average threshold
  20. 20. Morphological (Rank) Filtering threshold threshold “tophat” filter sequence
  21. 21. Multi-class Segmentation of Electron Microscopy Data process Mitochondria Other Cytoplasm process process process threshold threshold threshold threshold
  22. 22. Machine Learning for Image Segmentation threshold threshold threshold threshold threshold Arganda-Carreras, I., et al. (2017) Bioinformatics 33(15):2424-2426; Sommer, C., et al. (2011) doi: 10.1109/ISBI.2011.5872394; Ronneberger O., et al. (2015) doi: 10.1007/978-3-319-24574-4_28 edge average ... ... ... ... ... ... ... threshold threshold threshold threshold
  23. 23. Object Measurements Area [Pixels] Circularity Maximum [Gray Value] Sum [Gray Value] … 1906 0.738 219 242738 3361 0.823 136 262158 3884 0.905 98 259062
  24. 24. Calibrated Object Measurements Area [Pixels] Circularity Maximum Intensity [Gray Value] Sum Intensity [Gray Value] Area [ μm2 ] Circularity Maximum Concentration [ # / μm3 ] Molecules [ # ] Spatial Calibration Intensity Calibration Politi AZ, et al. (2018) Nat Protoc 13(6):1445-1464
  25. 25. Bioimage Analysis Results Exploration & Quality Control !
  26. 26. Results Exploration and Quality Control Precision = TP / ( TP + FP ) Recall = TP / ( TP + FN ) https://en.wikipedia.org/wiki/Precision_and_recall
  27. 27. Image „Ethics“ ● Microscopy „Ethics“ ?! ● Traditional image processing (Photoshop) is very different from Bioimage analysis
  28. 28. ● Java: ImageJ, Icy, Bio-Formats, BigDataViewer, imglib2, … ● Python: CellProfiler, scikit-image, ilastik, pytorch, … ● C++: ITK, openCV, vigra, cgal, ... ● MATLAB ● R ● KNIME Open-Source Bioimage Analysis Ecosystems not comprehensive January, 2019
  29. 29. Thank you! What questions do you have? Thanks for input and data: Advanced Light Microscopy Facility (EMBL), Pepperkok Lab (EMBL), Volker Hilsenstein (Monash University), Kota Miura (NEuBIAS), Ellenberg Lab (EMBL), Schwab Lab (EMBL), , Kreshuk Lab (EMBL), Jason Swedlow (Univ. Dundee), Anne Carpenter (Broad Institute), The www.image.sc community and many more...
  30. 30. Questions? A Basic Introduction to Bioimage Analysis Presenter: ChristianTischer (Centre for Bioimage Analysis (CBA), EMBL) 11/04/2019 Please write your questions in the questions window of the GoToWebinar application
  31. 31. UPCOMINGWEBINARS 11/04/2019 Registration and details http://www.corbel-project.eu/webinars

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