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MIST: Accurate and Scalable Microscopy Image Stitching Tool with Stage Modeling and Error Minimization
Joe Chalfoun1, Michael Majurski2, Tim Blattner2
1Information Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, 20878, MD, USA. joe.chalfoun@nist.gov.
Scientific Reports
|July 12, 2017
Summary
MIST (Microscopy Image Stitching Tool) rapidly stitches large microscopy images, improving accuracy for time-lapse studies. This advanced tool offers significant speed improvements for analyzing cell cultures.
Area of Science:
- Microscopy
- Computational Biology
- Image Analysis
Background:
- Automated microscopy enables large-scale imaging by stitching image tiles.
- Time-lapse studies require efficient stitching of multi-modal imaging data.
- Existing stitching tools can be slow and lack accuracy for complex datasets.
Purpose of the Study:
- To develop a rapid and accurate software tool for stitching large 2D time-lapse microscopy mosaics.
- To improve the efficiency and precision of automated microscopy image analysis.
- To provide a solution for processing terabytes of multi-channel imaging data.
Main Methods:
- Developed MIST (Microscopy Image Stitching Tool) utilizing multicore CPU/GPU computing.
- Estimated mechanical stage parameters and optimized translations to minimize stitching errors.
- Created 15 reference datasets with varying overlap for accuracy quantification.
Main Results:
- MIST processed terabytes of data 15 to 100 times faster than existing tools.
- Achieved an average centroid distance error less than 2% of a field of view (FOV).
- Demonstrated higher stitching accuracy compared to three open-source alternatives.
Conclusions:
- MIST provides a significant advancement in speed and accuracy for large-scale microscopy image stitching.
- The tool is effective for time-lapse studies of cell cultures and other biological specimens.
- MIST is available as an ImageJ plugin for broader accessibility.
