4EU+ Advanced image analysis with focus on - ImageJ, Arivis Vision 4D, SVI Huygens - MB100T01

• Anglický název: 4EU+ Advanced image analysis with focus on - ImageJ, Arivis Vision 4D, SVI Huygens
• Český název: 4EU+ Pokročilá analýza obrazu se zaměřením na software - ImageJ, Arivis Vision 4D, SVI Huygens
• Zajišťuje: Sekce biologie (31-101)
• Fakulta: Přírodovědecká fakulta
• Platnost: od 2023
• Semestr: zimní
• E-Kredity: 4
• Způsob provedení zkoušky: zimní s.:
• Rozsah, examinace: zimní s.:1/3, Z+Zk [DS]
• Počet míst: 20
• Minimální obsazenost: neomezen
• 4EU+: ano
• Virtuální mobilita / počet míst pro virtuální mobilitu: ano / 15
• Kompetence: 4EU+ Flagship 4
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Poznámka: povolen pro zápis po webu; předmět má cyklickou výuku
• Garant: Mgr. Zuzana Burdíková, Ph.D.
• Vyučující: Mgr. Zuzana Burdíková, Ph.D.; Ing. Martin Schätz, Ph.D.; Mgr. Zdeněk Švindrych, Ph.D.

Rekvizity pro virtuální mobilitu

Poslední úprava: Mgr. Zuzana Burdíková, Ph.D. (19.01.2023)

Information about the course

 Title – Advanced image analysis with a 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, and 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 8.1.2024 - 12.1.2022 of the WINTER semester of 2021, 

 

Syllabus

 

 

ImageJ: Theoretical introduction, an overview of graphical formats Bioformats, PSF, Nyquist, calculation, data export, shading correction, chromatic correction,, aligning, stitching, deconvolution, Segmentation (thresholding, watershed, object detection), Colocalization. Pearson Statistics, Manderson Statistics, 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, an overview of algorithms, measured vs theoretical PSF, Image formation, PSF, Convolution,  positivity constraint, regularization, artifacts

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, visualization 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 the 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

 

Matlab

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