Computational Analysis Of Microscopy Images
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Computational Analysis of Microscopy Images
This application-based guide fills a unique niche in the veterinary medical field by merging advanced computational techniques with the practical needs of veterinary pathology. With increasing prevalence of digital pathology, there is a burgeoning requirement to navigate veterinary professionals in the utilization of computational methods and the enhancement of diagnostic accuracy. This book caters to this demand, presenting the material in an accessible way to novices, technologists, and pathologists. Written from the perspective of a seasoned veterinary pathologist, it ensures that the techniques described are relevant and directly usable. Beginning with an exploration of microscopy fundamentals, the first part includes sample preparation, staining, and slide digitization. Subsequent chapters introduce readers to computational image analysis and the basics of image processing, tools, software, and successful integration of computational analysis into veterinary practice. Moreover, the book covers advanced topics such as image enhancement, reconstruction, quantitative analysis, and the application of machine learning and AI in microscopy image analysis. It provides insight into state-of-the-art imaging techniques like fluorescence and confocal microscopy, electron microscopy, and explores the innovations from nano to macro scales. The incorporation of case studies and sample workflows allows this work to demonstrate the practical benefits of computational image analysis in veterinary medicine, with improvements in diagnostic accuracy and workflow efficiency. It serves as a learning resource for continuous professional development, helping veterinary pathologists stay abreast of technological advances in image analysis. Serving veterinary professionals, pathologists, researchers, and computational biologists alike, this book is an essential resource for anyone looking to harness the power of computational tools and AI in veterinary medicine.
Computer Vision for Microscopy Image Analysis
Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts.Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information.Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation.This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. - Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery - Grasp the state-of-the-art approaches, especially deep neural networks - Learn where to obtain open-source datasets and software to jumpstart his or her own investigation
Bioimage Data Analysis Workflows
This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.