A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.
Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.
Duke Scholars
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- Software
- Microscopy, Fluorescence
- Image Processing, Computer-Assisted
- 3204 Immunology
- 3105 Genetics
- 3101 Biochemistry and cell biology
- 1108 Medical Microbiology
- 1107 Immunology
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Software
- Microscopy, Fluorescence
- Image Processing, Computer-Assisted
- 3204 Immunology
- 3105 Genetics
- 3101 Biochemistry and cell biology
- 1108 Medical Microbiology
- 1107 Immunology