The course will cover the basics in image data acquisition, processing and analysis including methods of stereology. Apart from a theoretical background, the emphasis will be put on practical experience.
The participants will learn how to use free software package Fiji for basic or more advanced analyses, how to evaluate co-localizations, analyse data from FRAP and electron microscopy data, how to track particles in images or segment objects, methods of artificial intelligence for image segmentation will be presented as well. They will learn how to improve data by deconvolution in Huygens software. We will have a lesson with Imaris software together with practical exercises. Stand-alone practical tasks using Fiji and Huygens will be an important part of the course as well.
The course follows the Microscopy Methods in Biomedicine, the previous attendance of this course, however, is not necessary.
I. The image data collection and image pre-processing
Essentials (pixel, voxel, resolution, levels of grey, matrix); Luminescence, intensity and color; Image as a data matrix; Human eye parameters in comparison to digital image parameters; Formats of image files (binary, greyscale, RGB, HSV, Lab); Basic filtration and image pre-processing; Look-up table, histogram, contrast and brightness, gamma correction.
Basic and advanced segmentation methods, including artificial intelligence (AI) approaches; Image deconvolution; Introduction into IJM (ImageJ Macro Language) and macro development.
Filtration and image preprocessing in 2D/3D using linear or morphological filters; Measurement and counting of segmented objects in 2D/3D; Area/volume measurement by pixel counting; Perimeter, length and surface area measurement using Crofton formula on binary images; Construction of geometric models from 3D data; Length and surface area measurement using 3D models.
II. Biological applications of the image analyses
Evaluation of co-localization of light microscopy images; Particle tracking; FRAP data analyses; Colocalization and clustering in electron microscopy data; Visualization and measurement of capillaries in 3D confocal data.
Introduction to stereology; Sampling in stereology; Cavalieri’s principle for measuring volume; Point-counting method; Methods for measuring length and surface area from thin sections; Methods based on focusing through thick sections: disector principle for counting three-dimensional particles (e.g., cells), methods for length measurement of spatial curves (e.g., capillaries) and surface area.