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
1. Microscopy Image Formation, Restoration and Segmentation 2. Detection and Segmentation in Microscopy Images 3. Visual Feature Representation in Microscopy Image Classi?cation 4. Object Tracking in Microscopy Images 5. Mitosis Detection in Microscopy Images and Image Sequences 6. Object Measurements from 2D Microscopy Images 7. Applications: Deep Learning-based Nuclei Segmentation and Classification in Histopathology Images with Applications in Imaging Genomics 8. Existing Open Source Datasets for Research
Mei Chen is Principal Researcher, Chief Science Advisor to CVP, Cloud & AI, Microsoft, Redmond, Washington, US. Her previous role was as Associate Professor in the Department of Informatics at the University of Albany State University of New York. She was also formerly the Intel Principal Investigator for the Intel Science & Technology Center on Embedded Computing that was headquartered at Carnegie Mellon University, bringing together researchers from Cornell, Intel, Georgia Tech, Penn State, UC Berkeley, UIUC, and UPenn. Dr Chen has has also held research and research lead positions at Intel Labs, HP Labs, and Sarnoff Corporation. Dr Chen's work in computer vision and biomedical imaging were nominated finalists for 5 Best Paper Awards and won 3. While at HP Labs, she successfully transferred her research in computational photography to 5 HP hardware and software products. She earned a Ph.D. in Robotics from the School of Computer Science at Carnegie Mellon University, and a M.S. and B.S. from Tsinghua University in Beijing, China