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This course provides a comprehensive introduction to advanced bioimage analysis, focusing on key software tools such as ImageJ, MIB, Arivis Vision 4D, and SVI Huygens. Through theoretical sessions and hands-on exercises, participants will learn fundamental and advanced concepts in image analysis, including image formation, segmentation, object detection, colocalization assessment, and deconvolution. Special emphasis is placed on the integration of artificial intelligence and deep learning tools for bioimage processing.
Last update: Sacherová Veronika, RNDr., Ph.D. (19.02.2025)
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For the final assessment, participants will prepare an individual project that demonstrates their ability to design and describe a bioimage analysis workflow. The project will contain several parts.
Last update: Sacherová Veronika, RNDr., Ph.D. (19.02.2025)
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Day 1 - Fundamentals of Bioimage Analysis Introduction to Bioimage analysis ● What is Bioimage analysis? ● Key features and applications Basic concepts ● Pixels/voxels, bit depth, dynamic range ● Resolution, sampling, and image artifacts Overview of software tools for Bioimage analysis Image processing fundamentals ● Common steps in object segmentation ● Classical machine learning approaches: Trainable Weka Segmentation ● Object detection methods: Watershed and Connected Components Analysis Hands-on case study: Reproducibility in Bioimage analysis ● Comparing manual vs. automated object counting Introduction to AI in Bioimage analysis Day 2: Processing Large-Scale Microscopy Datasets Pre-processing of (not only) volume electron microscopy (VEM) data ● Stitching and stack alignment using FIJI's TrackEM Introduction to Microscopy Image Browser (MIB) ● Image I/O, bit depth conversion, filtering, alignment, and stitching Segmentation strategies in MIB ● Part I: Layers, superpixel clustering, and GraphCut segmentation ● Part II: Deep learning tools (DeepMIB, Segment Anything Model) Day 3: Bioimage Analysis workflows with ArivisPro First steps with ArivisPro Handling large image datasets ● Tile sorting and stitching Lightsheet Microscopy: Introduction and applications 3D segmentation workflows ● Machine learning-based segmentation ● Blobs finder pipeline ● Magic Wand tool for segmentation 3D Object Tracking & Visualization Colocalization analysis in ArivisPro (theory & hands-on) Day 4: Image deconvolution & Data visualization Principles of Image Deconvolution ● Theory behind deconvolution ● Hands-on practice using SVI Huygens Data Visualization & Reporting ● Best practices for presenting results ● Hands-on session using Python (Pandas, Matplotlib, Seaborn) and Google Colab Day 5: Emerging Trends in Bioimage Analysis Bioimage analysis communities & collaboration Deep learning in Bioimage analysis ● Introduction to AI-driven tools ● Noise2Void for image denoising ● StarDist for star-convex object segmentation ● Hands-on practice Last update: Sacherová Veronika, RNDr., Ph.D. (19.02.2025)
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