Computer Vision for Microscopy Image Analysis
- 1st Edition - December 1, 2020
- Imprint: Academic Press
- Editor: Mei Chen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 4 9 7 2 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 4 9 7 3 - 7
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 Mi… Read more
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Request a sales quoteAre 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
Researchers and graduate students in computer vision, biomedical engineering, image science, and biological and medical science interested or working in biological or biomedical image analysis
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1: A biologist's perspective on computer vision
- Abstract
- 1: Thesis
- 2: Audience
- 3: Aim
- 4: Vision
- 5: Why biologists need computer vision experts
- 6: Why computer scientists need biologists
- 7: The limits of human visual perception from digital images
- 8: Quantitative phenotypic traits, high-content analysis
- 9: Different metrics for career advancement
- 10: The collaboration relationship
- 11: Biologists interacting with computer vision products
- 12: Current needs in biology
- 13: Conclusions and future perspectives
- Chapter 2: Microscopy image formation, restoration, and segmentation
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Image formation
- 3: Optics-based image restoration
- 4: Cell segmentation
- 5: Conclusion
- Chapter 3: Detection and segmentation in microscopy images
- Abstract
- 1: Introduction
- 2: Cell detection and segmentation in optical microscopy images
- 3: Segmentation of neuronal structures in EM images
- 4: Conclusion
- Chapter 4: Visual feature representation in microscopy image classification
- Abstract
- 1: Introduction
- 2: Fisher vector representation
- 3: Separation-guided dimension reduction
- 4: Supervised intraembedding
- 5: Conclusions
- Chapter 5: Cell tracking in time-lapse microscopy image sequences
- Abstract
- 1: Traditional cell tracking approaches
- 2: Deep learning-based cell tracking approaches
- 3: Metrics for evaluating and comparing cell tracking performance
- 4: A note on particle tracking
- 5: Future directions for computer vision-based cell tracking
- Chapter 6: Mitosis detection in biomedical images
- Abstract
- Acknowledgment
- 1: Mitosis process and the detection problem
- 2: Medical image for mitosis detection
- 3: Mitosis detection approaches
- 4: Conclusion
- Chapter 7: Object measurements from 2D microscopy images
- Abstract
- Acknowledgments
- Disclaimer
- 1: Background
- 2: Introduction to 2D image measurements
- 3: Approach to numerical evaluations of feature variability and feature-based classification
- 4: Integration of open-source libraries for 2D image measurements
- 5: Image features in scientific use cases
- 6: Variability of image features
- 7: Feature-based classification
- 8: Summary
- Appendix: Online information about the work
- Chapter 8: Deep learning-based nuclei segmentation and classification in histopathology images with application to imaging genomics
- Abstract
- 1: Joint nuclei segmentation and classification in histopathology images
- 2: Applications to imaging genomics
- 3: Conclusion
- Chapter 9: Open data and software for microscopy image analysis
- Abstract
- 1: Data is oxygen
- 2: Open data
- 3: Open software
- Index
- Edition: 1
- Published: December 1, 2020
- Imprint: Academic Press
- No. of pages: 228
- Language: English
- Paperback ISBN: 9780128149720
- eBook ISBN: 9780128149737
MC
Mei Chen
Mei Chen is a principal research manager at Microsoft. She was an associate professor in the Electrical and Computer Engineering Department and director for the Information Science PhD Program at the State University of New York, Albany. She was the founding chair for the Workshop on Computer Vision for Microscopy Image Analysis that has been held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition since 2016. Mei has published extensively in computer vision and biomedical image analysis. Her work was nominated as a finalist for six Best Paper Awards, for which she won three. She earned a PhD in robotics from the School of Computer Science, Carnegie Mellon University, and an MS and BS from Tsinghua University, Beijing, China.
Affiliations and expertise
Principal Research Manager, Microsoft, Redmond, Washington, USARead Computer Vision for Microscopy Image Analysis on ScienceDirect