Bioimage Data Analysis Workflows ? Advanced Components and Methods
Description
This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis.
Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics.
The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS - the Network of European Bioimage Analysts.
This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images.
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Persons
Dr. Kota Miura is a Freelance Bioimage Analyst and works with various research groups and companies in Europe, for teaching, consulting, and collaborations. He also is affiliated with the Nikon Imaging Center at the University of Heidelberg and is the Vice-Chair of NEUBIAS (the Network of European Bioimage Analysts).
Natasa Sladoje is Professor in Computerized Image Processing at the Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden. She is coordinator of a Master's programme in Image Analysis and Machine Learning at Uppsala University and has a long experience in education in this field. Her research interests include theoretical development of image analysis methods for robust image processing with high information preservation, robust methods for image comparison and registration, as well as development and applications of machine and deep learning methods particularly suitable for biomedical image analysis. She is the founder and leader of MIDA - Methods for Image Data Analysis - research group at Department of Information Technology, Uppsala University, and an active member of NEUBIAS - the European Network of Bioimage Analysis.
Content
Introduction.- Batch Processing Methods in ImageJ.- Python: Data Handling, Analysis and Plotting.- Building a Bioimage Analysis Workflow Using Deep Learning.- GPU-Accelerating ImageJ Macro Image Processing Workflows Using CLIJ.- How to Do the Deconstruction of Bioimage Analysis Workflows: A Case Study with SurfCut.- i.2.i. with the (Fruit) Fly: Quantifying Position Effect Variegation in Drosophila Melanogaster.- A MATLAB Pipeline for Spatiotemporal Quantification of Monolayer Cell Migration.