Toward lower contrast computer vision in vivo flow cytometry.
Toward lower contrast computer vision in vivo flow cytometry.
复制标题
迈向低对比度计算机视觉体内流式细胞术。
DOI：
10.1109/embc.2014.6944564
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Niedre,Mark
中科院分区：
文献类型：
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作者：
Markovic,Stacey;SiyuanLi;TianxueZhang;Niedre,Mark
生物医学研究中有许多应用，其中循环细胞（CC）的检测和计数非常重要。现有技术涉及抽取和富集血液样品并离体分析它们。最近，已经开发了小动物“体内流式细胞术”（IVFC）技术，其中检测和计数流过小动脉（耳朵、视网膜）的荧光标记的细胞。我们最近开发了一种新的高灵敏度IVFC技术，称为“计算机视觉（CV）-IVFC”。在这里，用宽视场视频速率近红外（NIR）荧光相机监测小鼠耳朵中的大循环血量。用膜染料标记细胞，并在噪声图像序列中检测和跟踪细胞。该技术允许在体内计数CC，总体灵敏度优于10个细胞/mL。然而，我们实验室正在进行的一个感兴趣的领域是针对低对比度成像条件优化系统，例如当CC被弱标记时，或者在具有可见染料的较高背景自发荧光的情况下。为此，我们开发了一种新的光流体模模型来控制自体荧光强度和物理结构，以更好地模拟在小鼠中观察到的条件。我们从一系列具有不同对比度的体模中获取图像序列，并分析像素强度的分布，结果表明我们可以生成与体内相似的条件。我们的特点是我们的CV-IVFC算法在这些幻影的灵敏度和误报率方面的性能。在低对比度条件下使用该模型优化仪器和算法是我们实验室正在进行的工作的主题。
There are many applications in biomedical research where detection and enumeration of circulating cells (CCs) is important. Existing techniques involve drawing and enriching blood samples and analyzing them ex vivo. More recently, small animal “in vivo flow cytometry” (IVFC) techniques have been developed, where fluorescently-labeled cells flowing through small arterioles (ear, retina) are detected and counted. We recently developed a new high-sensitivity IVFC technique termed “Computer Vision(CV)-IVFC”. Here, large circulating blood volumes were monitored in the ears of mice with a wide-field video-rate near-infrared (NIR) fluorescent camera. Cells were labeled with a membrane dye and were detected and tracked in noisy image sequences. This technique allowed enumeration of CCs in vivo with overall sensitivity better than 10 cells/mL. However, an ongoing area of interest in our lab is optimization of the system for lower-contrast imaging conditions, e.g. when CCs are weakly labeled, or in the case higher background autofluorescence with visible dyes. To this end, we developed a new optical flow phantom model to control autofluorescence intensity and physical structure to better mimic conditions observed in mice. We acquired image sequences from a series of phantoms with varying levels of contrast and analyzed the distribution of pixel intensities, and showed that we could generate similar conditions to those in vivo. We characterized the performance of our CV-IVFC algorithm in these phantoms with respect to sensitivity and false-alarm rates. Use of this phantom model in optimization of the instrument and algorithm under lower-contrast conditions is the subject of ongoing work in our lab.