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

被引:4
|
作者
Lu, Guolan [1 ,2 ,3 ]
Baertsch, Marc A. [2 ,3 ,4 ]
Hickey, John W. [2 ,3 ]
Goltsev, Yury [2 ,3 ]
Rech, Andrew J. [2 ,3 ]
Mani, Lucas [1 ]
Forgo, Erna [3 ]
Kong, Christina [3 ]
Jiang, Sizun [5 ]
Nolan, Garry P. [2 ,3 ]
Rosenthal, Eben L. [1 ,6 ]
机构
[1] Stanford Univ, Sch Med, Dept Otolaryngol, Stanford, CA 94305 USA
[2] Stanford Univ, Sch Med, Dept Microbiol & Immunol, Stanford, CA 94305 USA
[3] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA 94305 USA
[4] Heidelberg Univ Hosp, Dept Hematol Oncol & Rheumatol, Heidelberg, Germany
[5] Harvard Med Sch, Ctr Virol & Vaccine Res, Beth Israel Deaconess Med Ctr, Boston, MA 02115 USA
[6] Vanderbilt Univ, Med Ctr, Dept Otolaryngol, Nashville, TN 37235 USA
来源
FRONTIERS IN IMMUNOLOGY | 2022年 / 13卷
基金
美国国家卫生研究院; 比尔及梅琳达.盖茨基金会;
关键词
image processing; highly multiplexed imaging; CODEX imaging; image deconvolution; drift compensation; GPU acceleration; parallel computing; big data;
D O I
10.3389/fimmu.2022.981825
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
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.
引用
收藏
页数:13
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