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Lecturer(s)
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Sehadová Hana, Mgr. Ph.D.
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Bondar Alexey, Mgr. Ph.D.
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Course content
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Content of lectures: 1. Scanning and digitalization of an image: basic principles and terminology, principle of color digitalization (Sehadová) 2. Types of imaging data and image preprocessing and deconvolution (Sehadová) 3. Types of useful software and programming languages. Strength and weaknesses of different approaches to specific imaging data types. 4. Bias, artifacts, pitfalls, and common precautions in image analysis. 5. Metadata, Image repositories. Statistical evaluation of the imaging data 6. Seminar 1: Brief progress presentation on the creative project or own data analysis 7. Introduction to Napari software 8. Introduction to Matlab software 9. Introduction to Python software 10. Introduction to Imaris and Amira software 11. Machine learning and AI in image analysis 12. Seminar 2: Final presentation of the creative project or analysis of own data 13.Ethical principles of image processing in biological research Content of tutorials/seminar: 1. Basic analysis in Adobe Photoshop - part I (Sehadová) 2. Basic analysis in Adobe Photoshop - part II (Sehadová) 3. Fiji/ImageJ - the workhorse of image analysis 4. Fiji/ImageJ - plug-ins and macros 5. Cell Profiler - a complement to ImageJ for advanced processing 6. Practical exams 1 - Analyses in Adobe Photoshop and ImageJ - independent execution of specified analyses 7. Napari - new Python-based software of choice 8. Matlab as a tool for image analysis and its area of application 9. Python - a versatile solution for different analysis tasks 10. Imaris or Amira - commercial user-friendly processing and 3D reconstruction 11. Machine learning and AI for analysis 12. Seminar 2: Final presentation of the creative project or analysis of own data 13. Practical exams 2 - Analyses in advanced software - independent execution of specified analyses
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The course introduces students in detail to the basic principles and rules of microscopic image analysis. It will also introduce a range of both freely available and commercial software suitable for scientific image analysis. In the practical part of the course, students will try out and partially master image analysis in these programs. The course also includes independent work on a creative project.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Conditions for passing the credit: 1) participation in practical exercises - maximum one unexcused absence 2) passing the practical examinations 1 and 2 Conditions for passing the exam: 1) passing the credit 2) oral exams (examinator Bondar, Sehadova)
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Recommended literature
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https://assets.thermofisher.com/TFS-Assets/MSD/Product-Guides/user-guide-amira-software.pdf.
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https://docs.python.org/3/tutorial/.
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https://helpx.adobe.com/photoshop/view-all-tutorials.html.
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https://imagej.net/tutorials.
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https://imaris.oxinst.com/tutorials.
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https://napari.org/stable/tutorials/index.html.
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https://www.mathworks.com/support/learn-with-matlab-tutorials.html.
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http://www.openmicroscopy.org/site.
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