Skip to main content

A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.

Publication ,  Journal Article
Lu, G; Baertsch, MA; Hickey, JW; Goltsev, Y; Rech, AJ; Mani, L; Forgó, E; Kong, C; Jiang, S; Nolan, GP; Rosenthal, EL
Published in: Frontiers in immunology
January 2022

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

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Frontiers in immunology

DOI

EISSN

1664-3224

ISSN

1664-3224

Publication Date

January 2022

Volume

13

Start / End Page

981825

Related Subject Headings

  • Software
  • Microscopy, Fluorescence
  • Image Processing, Computer-Assisted
  • 3204 Immunology
  • 3105 Genetics
  • 3101 Biochemistry and cell biology
  • 1108 Medical Microbiology
  • 1107 Immunology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lu, G., Baertsch, M. A., Hickey, J. W., Goltsev, Y., Rech, A. J., Mani, L., … Rosenthal, E. L. (2022). A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data. Frontiers in Immunology, 13, 981825. https://doi.org/10.3389/fimmu.2022.981825
Lu, Guolan, Marc A. Baertsch, John W. Hickey, Yury Goltsev, Andrew J. Rech, Lucas Mani, Erna Forgó, et al. “A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.Frontiers in Immunology 13 (January 2022): 981825. https://doi.org/10.3389/fimmu.2022.981825.
Lu G, Baertsch MA, Hickey JW, Goltsev Y, Rech AJ, Mani L, et al. A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data. Frontiers in immunology. 2022 Jan;13:981825.
Lu, Guolan, et al. “A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.Frontiers in Immunology, vol. 13, Jan. 2022, p. 981825. Epmc, doi:10.3389/fimmu.2022.981825.
Lu G, Baertsch MA, Hickey JW, Goltsev Y, Rech AJ, Mani L, Forgó E, Kong C, Jiang S, Nolan GP, Rosenthal EL. A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data. Frontiers in immunology. 2022 Jan;13:981825.

Published In

Frontiers in immunology

DOI

EISSN

1664-3224

ISSN

1664-3224

Publication Date

January 2022

Volume

13

Start / End Page

981825

Related Subject Headings

  • Software
  • Microscopy, Fluorescence
  • Image Processing, Computer-Assisted
  • 3204 Immunology
  • 3105 Genetics
  • 3101 Biochemistry and cell biology
  • 1108 Medical Microbiology
  • 1107 Immunology