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This project develops image analysis algorithms and systems to process microscopy images that record the proliferation history of biological specimens and evaluate their behaviors that respond to different culturing conditions, therefore, deciphering complex biological processes and accelerating the advance of biological discovery. The research combines techniques of physical optics, computer vision and crowdsourcing to bring a breakthrough to microscopy imaging and microscopy image analysis. The developments of such technologies transform the image-based biology research from subjective to a rigorous, quantitative, and efficient manner. The research team also seeks to promote interdisciplinary collaboration between biological imaging and computer vision, integrate the research outcomes into education activities, and disseminate the project to a wide audience via web, K-12 group, conferences and industry collaborations.

Previous microscopy image analysis methods do not consider the particular image formation process and treat them in the same manner as general natural images, causing many difficulties or failures in the image analysis. This project addresses the challenges in a principally different way by investigating the theoretical foundation of microscopy optics. The computational imaging models of microscopes are derived and used to restore artifact-free images and extract optics-oriented image features, which makes the automated image analysis fundamentally correct and easy. The models are further used to enhance the microscope’s functionalities including calibration and virtual microscopy. A cyber-enabled research community is being established within which active learning and crowd-computing are leveraged to improve the algorithm performance and biological discovery.

Updates are available from http://web.mst.edu/~yinz/.

Detailed Award Information

Award Information:
Title: CAREER: Microscopy Image Analysis to Aid Biological Discovery: Optics, Algorithms, and Community
ID: 2019967
Effective Date: 08/31/2019
Expiration Date: 09/30/2020
Amount: $244,075

Institution Information:
Name:
City:
State:
Country: United States
MSI: Other Institution

Investigator Information:
Role Code: Principal Investigator
Name: Zhaozheng Yin
Email Address: zhaozheng.yin@stonybrook.edu

Organization Information:
Directorate: 4900
Division: NSF

Program Information:
Code: 4900
Text: Robust Intelligence