To investigate biological processes, bioimage informatics emerges as a growing field on the interface between microscopy, signal-processing, and computer science. The recent microscopes are producing large volumes of high-resolution multidimensional data (up to 5D). Therefore, algorithms and software tools are needed to automatically extract quantitative data from these images.

The course gives the theoretical concepts and practical aspects of the most common image reconstruction and image analysis techniques. It explains how to code algorithms and to deploy software tools to build an automatic analysis workflow (mainly in ImageJ/Fiji). The lecture is tailored to the needs of life sciences and driven by biological questions.

Addressed topics include (but not restricted to): presentation of microscopy modalities, digital images, multi-dimensional data (3D, time, multiple channels) manipulation, 3D image-processing algorithms, 5D visualization, reconstruction, deconvolution, denoising, stitching, visual feature detection, segmentation, active contours, image analysis workflow, pixel classification, machine learning, deep learning for image analysis, and tracking of particles.

The course is composed of lectures, workshops, practives, and a mini-project. A personal laptop is recommended to run (open-source) image analysis software.