High-throughput microscopy enables researchers to acquire thousands of images automatically over a short time, making it possible to conduct large-scale, image-based experiments for biological or biomedical discovery. However, visual analysis of large-scale image data is a daunting task. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques. Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, demonstrating how they can be effectively applied to biological and medical data. The reader of the book will learn: How computer vision analysis can automate and enhance human assessment of microscopy images for discovery The important steps in microscopy image analysis State-of-the-art methods for microscopy image analysis including machine learning and deep neural network approaches This reference on the state-of-the-art computer vision methods in microscopy image analysis is suitable for researchers and graduate students interested in analyzing microscopy images or for developing toolsets for general biomedical image analysis applications. Each topic contains a comprehensive overview of the field, followed by in-depth presentation of a state-of-the-art approach Perspectives and content contributed by both technologists and biologists Tackles specific problems of detection, segmentation, classification, tracking, cellular event detection Contains the fundamentals of object measurement in microscopy images Contains open source data and toolsets for microscopy image analysis on an accompanying website.