A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.
A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.
复制标题
针对大规模多路荧光显微镜数据的实时GPU加速并行化图像处理器。
DOI：
10.3389/fimmu.2022.981825
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
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作者：
高度多重的单细胞成像彻底改变了我们对与健康和疾病相关的空间细胞相互作用的理解。随着抗原数量、区域大小和样本量的不断增加，多重荧光成像实验通常会产生数 TB 的数据。快速准确地处理这些大规模、高维成像数据对于确保细胞类型的可靠分割和识别以及细胞邻域的表征和机制见解的推断至关重要。在这里，我们描述 RAPID，一种用于大规模多重荧光显微镜数据的实时 GPU 加速并行图像处理软件。 RAPID 对大规模、高维荧光成像数据进行去卷积，通过轴向和横向漂移校正来缝合和配准图像，并最大限度地减少组织自发荧光（例如红细胞引入的自发荧光）。结合开源 CUDA 驱动、GPU 辅助反卷积产生的结果与收费商业软件类似。与我们之前的图像处理流程相比，RAPID 减少了数据处理时间和伪影，并提高了图像对比度和信噪比，从而为大规模、多重荧光成像数据的准确和稳健分析提供了有用的工具。
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.