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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
Arivis Vision4D: Introduction to Arivis Vision4D, Overview of biological applications, batch processing Practical demonstration
Collab and Statistics Theoretical part: Python introduction, Python/Collab, Statistics - best practice Practical part : Pandas
Matlab
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