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Computer Vision for Microscopy Image Analysis. Computer Vision and Pattern Recognition

  • ID: 4612898
  • Book
  • December 2020
  • 228 Pages
  • Elsevier Science and Technology

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

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  1. A biologist's perspective on computer vision
  2. Microscopy image formation, restoration and segmentation
  3. Detection and segmentation in microscopy images
  4. Visual feature representation in microscopy image classification
  5. Cell tracking in time-lapse microscopy image sequences
  6. Mitosis detection in biomedical images
  7. Object measurements from 2D microscopy images
  8. Deep learning-based nuclei segmentation and classification in histopathology images with application to imaging genomics
  9. Open data and software for microscopy image analysis
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Chen, Mei
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.
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