| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | July 23, 2014 |
| Latest Amendment Date: | June 26, 2015 |
| Award Number: | 1421943 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2014 |
| End Date: | October 31, 2018 (Estimated) |
| Total Intended Award Amount: | $370,151.00 |
| Total Awarded Amount to Date: | $370,151.00 |
| Funds Obligated to Date: |
FY 2015 = $170,151.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1 SILBER WAY BOSTON MA US 02215-1703 (617)353-4365 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
111 Cummington Mall Boston MA US 02215-2411 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
ADVANCES IN BIO INFORMATICS, Information Technology Researc, Cross-BIO Activities, Robust Intelligence |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Sequences of microscopy images of live cells are analyzed by cell biologists to understand cellular processes, for example, to prevent cancer or design bio-materials for wound healing. Research progress is slowed or compromised when scientists find the image analysis efforts too labor-intensive to do themselves and the automation methods too numerous, unreliable, or difficult to use. The project develops image-analysis software to leverage human and computer resources together, in particular on the internet, to create high-quality image interpretations. Live-cell imaging studies support basic research to understand cellular processes and design biomaterials. The work on statistically significant performance evaluation can have broad impact on the research methodology in computer vision.
The research explores how human and computer resources can be leveraged together, in particular on the internet, to interpret images and videos of cells. Initially, an expansive benchmark study of detection, segmentation, and tracking algorithms for analyzing images of live cells is conducted. Computer-vision approaches to address the major challenges for existing algorithms are then developed, for example, to interpret the emergence of new cells due to mitosis in time-lapse microscopy videos. Methods are designed for quantifying the quality of automatically and manually obtained annotations and the variability between multiple annotations. A tool is built to effectively and efficiently use the expertise of domain specialists, in particular, cell biologists, to judge and select automated methods that analyze cell images. Crowd-sourcing experiments in which internet workers analyze images are designed and conducted. The quality of these lay workers' annotations is compared to the quality of annotations by domain experts and automated methods. Finally, a machine learning system is developed that automatically determines which types of cell images or videos can be analyzed accurately with or without human involvement.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project addressed image analysis problems that are difficult for a computer to solve automatically and extremely time consuming for an expert to solve manually. The project developed methods for combining computing resources with expert or non-expert work to interpret single images and image sequences of living cells. Non-experts who work with crowdsourcing platforms on the internet participated in experiments with phase contrast microscopy and fluorescence microscopy images of cells. The project developed several systems. One system automatically decides, for a batch of images, when to replace humans with computers to create coarse outlines of cells, which are required to initialize automated tools. Another system decides when to replace computers with humans to create final, fine-grained cell outlines. Experiments demonstrated the advantage of relying on a mix of human and computer efforts over relying on either resource alone for annotating objects in various diverse datasets. The project also showed how the allocations of the number of internet workers to an annotation task can be computed optimally based on task features alone, without using worker profiles. A machine learning system was trained to predict an optimal number of crowd workers needed to maximize the accuracy of the annotations. The computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy.
Last Modified: 03/06/2019
Modified by: Margrit Betke
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