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

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. 11/04/2019 2
This webinar is being recorded

3. AUDIENCE Q&A SESSION
11/04/2019 3
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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. 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. 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. Bioimage Analysis
Christian Tischer
Centre for Bioimage Analysis (CBA)
European Molecular Biology Laboratory (EMBL)

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. Quantification, Reproducibility & Statistics
Control Treated
**

10. Image = { Pixels }
x
y
Pixel = ( value, coordinate )

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. 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. Image Formation: Blur and Noise

14. Image Restoration: Counteracting Image Formation
Formation:
Noise, Blur
Restoration:
Denoising, Deblurring
Weigert M (2018) Nat Methods 15(12):1090-1097

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. Image Processing: Convolution Filters
edge
+1 0 -1
+1 0 -1
+1 0 -1
+ 89 + 86 + 93
- 46 - 57 - 49
= 116

17. Image Segmentation
segment
Gray values Objects

18. Image Segmentation: Example Workflow
1
2
threshold
connected
component
analysis

19. Image Processing Facilitates Image Segmentation
threshold
average
threshold

20. Morphological (Rank) Filtering
threshold threshold
“tophat” filter sequence

21. Multi-class Segmentation
of Electron Microscopy Data
process
Mitochondria
Other
Cytoplasm
process
process
process
threshold
threshold
threshold
threshold

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. 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. 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. Bioimage
Analysis
Results Exploration
& Quality Control
!

26. Results Exploration and Quality Control
Precision =
TP / ( TP + FP )
Recall =
TP / ( TP + FN )
https://en.wikipedia.org/wiki/Precision_and_recall

27. Image „Ethics“
● Microscopy „Ethics“ ?!
● Traditional image processing (Photoshop) is very
different from Bioimage analysis

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. 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. 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. UPCOMINGWEBINARS
11/04/2019
Registration and details
http://www.corbel-project.eu/webinars