Synopses & Reviews
Synopsis
Computer Vision for Microscopy Image Analysis provides a broad and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, showing how they can be applied to biological and medical data. Topics covered include sections on 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, how high-throughput microscopy enables researchers to automatically acquire thousands of images over a matter of hours, and more.
- Contains a general overview on each topic that is followed by an in-depth presentation of a state-of-the-art approach
- Includes perspectives and content contributed by both technologists and biologists
- Covers specific problems of segmentation and mitosis detection
- Introduces the fundamentals of tracking and 3D analysis
- Presents open source data and toolsets for microscopy image analysis on an accompanying website
Synopsis
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
Synopsis
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.