SubjectsSubjects(version: 901)
Course, academic year 2022/2023
Advanced image analysis with focus on - ImageJ, Arivis Vision 4D, SVI Huygens - MB100T01
Title: Advanced image analysis with focus on - ImageJ, Arivis Vision 4D, SVI Huygens
Czech title: Pokročilá analýza obrazu se zaměřením na software - ImageJ, Arivis Vision 4D, SVI Huygens
Guaranteed by: Biology Section (31-101)
Faculty: Faculty of Science
Actual: from 2022
Semester: winter
E-Credits: 2
Examination process: winter s.:
Hours per week, examination: winter s.:0/5 C [days/semester]
Capacity: 15
Min. number of students: unlimited
Virtual mobility / capacity: yes / 15
Key competences: 4EU+ Flagship 4
State of the course: taught
Language: English
Note: enabled for web enrollment
the course is taught as cyclical
Guarantor: Mgr. Zuzana Burdíková, Ph.D.
Teacher(s): Mgr. Zuzana Burdíková, Ph.D.
Ing. Martin Schätz, Ph.D.
Mgr. Ondřej Šebesta
Opinion survey results   Examination dates   Schedule   
Rekvizity pro virtuální mobilitu
Last update: Mgr. Zuzana Burdíková, Ph.D. (05.10.2021)

Information about the course

 Title – Advanced image analysis with focus on - ImageJ, Arivis Vision 4D,     Imaris, SVI Huygens, MATLAB

 Code – MB100T01

 Guarantor – Msc. Zuzana Burdíková, Ph.D

 All lecturers – Msc. Zuzana Burdíková, Ph.D, Ing. Martin Schätz, Ph.D. , Msc. Ondřej Šebesta

 Faculty, department – Faculty of Science, Laboratory of Fluorescent and Confocal Microscopy , Charles University

 Credits – 02 ECTS

 Language of instruction - English

 Flagship and/or transversal skills – Flagship 4, Critical thinking

 Capacity - 15

 Examination – project

 Minimal requirements, prerequisites, conditions for selection and enrolment of students: Basic knowledge of Image J is required. The course is aimed at explaining the workflow in Image Analysis, processing and it is assumed that the student is interested in Image Analysis.

 Virtual mobility - yes

 How the course will be taught (one week) and the starting date –block; on the 10.1.2022 - 14.2.2022 of the WINTER semester of 2021





ImageJ : Theoretical introduction, overview of graphical formats Bioformats, PSF, Nyquist, calculation, data export, shading correction, chromatic correction,, aligning, stitching, dekonvolution, Segmentation (thresholding, watershed, object detection), Colocalization. Pearson Statistics, Manderson Statistic, Statistical tests, FIJI, Macros, Plug-ins

Practical part: a) Filters, Segmentace (threshold, WEKA),

b) Deep Learning modely (STARDist, Noise2Void a další) , 

c)Quantification.Colocalization, Macros, workflow



Huygens : Theoretical background, overview of algorithms , measured vs theoretical PSF, Image formation, PSF, Convolution,  positivity constraint, regularization, artefactsI

Practical part:  deconvolution, stitching in Huygens praktické cvičení - Huygens, FIJI - dataset

  1. export do FIJI

  2.  tracking Huygens

  3.  stitching


Arivis Vision4D: Introduction to Arivis Vision4D, Overview of biological applications, batch processing

Practical demonstration

  1. ZEN and Arivis Volume Fusion with Arivis Vision4D

  2. Importing images with arivis Vision4D, Importing Complex Images with arivis Vision4D

  3. Channel Colors

  4. Time series with arivis Vision4D

  5. Manual stitching and alignment using the tile sorter

  6. Save important views with bookmarks and create high resolution screenshots

  7. Create movies with the storyboard and video export

  8. Split view mode, projection gallery and info viewer

  9. Color handling, visualisation settings

  10. New Analysis Pipeline User Interface with arivis Vision4D, segmentation, filters, custom features

  11. Watershed vs Blob Finder Segmenters with Arivis Vision 4D

  12. Annotations & Annotation Settings

  13. Copy & Transform Objects

  14. 3D Measurements

  15. Basics of Parent-Child Analysis

  16. Tracking

  17. Membrane Based Segmenter

  18. Import & Saving of Analysis Pipelines arivis Vision4D

  19. Distance Measurements

  20. Data sets export of next processing  (statistics) 

  21. Max int.projection, transparency mode 

  1. Working with Results

  1. Table from workflow for MatLab & Python


Collab and Statistics Theoretical part :Python introduction, Python/Collab, Statistics - best practice

Practical part : Pandas

  1. Bokeh

  2. High level plotting

  3. Statistics

  4. Big Data (table) statistics



  1. Representation of numbers

  2. Symbolic mathematics

  3. Algebra

  4. Variables

  5. Program management

  6. 1D Visualization

  7. TeX

  8. Import image data

  9. 2D visualization

  10. 3D visualization

  11. Toolboxes

  12. Introduction to GUI

Charles University | Information system of Charles University |