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Biological Engineering - ImageJ Intensive course - MB100P10

Title:	4EU+ Biological Engineering - ImageJ Intensive course
Czech title:	Bioengineering - kurz ImageJ
Form of teaching:	lecture+practicals
Guaranteed by:	Biology Section (31-101)
Faculty:	Faculty of Science
Actual:	from 2026
Duration in semesters:	1
Semester:	summer
E-Credits:	3
Examination process:	summer s.:
Hours per week, examination:	summer s.:1/2, Ex [HT]
Capacity:	20
Maximum number of enrolled students:	20
Min. number of students:	unlimited
4EU+:	yes
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	English
Note:	enabled for web enrollment the course is taught as cyclical

Guarantor:	Mgr. Ondřej Šebesta

Opinion survey results Schedule

Annotation -

This course will provide a comprehensive understanding of the usage of the open-source software ImageJ for
scientific image analysis. The course will cover everything from basic manipulations to custom scripting. Students
will learn how to create functional workflows for the analysis. The course is suitable for total beginners as well as
for intermediate users. The course will be held in hybrid mode for a maximum of 20 students in personal
attendance and virtually unlimited students online.

Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)

Literature -

ImageJ study

https://imagej.nih.gov/ij/docs/examples/index.html

Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)

Course assessment methods and requirements for successful completion, grading scheme -

Examination based on project report.

Last update: Sacherová Veronika, RNDr., Ph.D. (11.05.2023)

Syllabus -

Lesson 1: Image Formation in microscopy modalities, resolution and super-resolution, noise, image dimensions, file formats, workflows, image preprocessing, image restoration, image processing, segmentation, object classification, measurements, statistics, data visualization, data management, FIJI introduction.

Lesson 1: Basic image handling in FIJI

Lesson 2: Preparing images for analysis

Lesson 3: Basic measurements and image segmentation

Lesson 4: Machine learning techniques and advanced image processing and segmentation

Lesson 5: CLIJ GPU accelerated analysis, FIJI customization

Lesson 6: Introduction to macro language, scripting, and batch processing

Lesson 7: Programming fundamentals, writing scripts in macro language

Lesson 8: Writing custom scripts for batch analysis

Lesson 9: Colocalization analysis

Lesson 10: Advanced microscopy techniques analysis and image processing

Lesson 11: Implementation FIJI in large workflows, using imageJ in computing cluster

Lesson 12: Students projects presentation

Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)

Learning outcomes -

Learning Outcomes

Upon completion of the course, the student will be able to:

Explain the principles of digital image formation in microscopy, including key parameters such as resolution, noise, and bit depth.
Navigate the ImageJ/FIJI user interface and utilize tools for calibration, visualization, and basic manipulation of multidimensional image data.
Evaluate the quality of microscopy data and apply appropriate preprocessing methods (e.g., background correction, image registration) to eliminate artifacts.
Perform quantitative analysis of biological images using segmentation, object classification, and measurement of regions of interest (ROI).
Design and create custom scripts in the macro language to automate analysis and enable batch data processing.
Construct complex analytical workflows for advanced tasks, such as colocalization analysis or 3D microscopy data processing.

Last update: Šebesta Ondřej, Mgr. (29.01.2026)

The course does not include work placement