Department of Electrical and Computer Engineering at Rutgers, The State University of New Jersey, New Brunswick, USA.
Global Health Institute at Rutgers, The State University of New Jersey, New Brunswick, USA.
Analyst. 2023 Nov 20;148(23):6036-6049. doi: 10.1039/d3an01166f.
Micro-nanoparticle and leukocyte imaging find significant applications in the areas of infectious disease diagnostics, cellular therapeutics, and biomanufacturing. Portable fluorescence microscopes have been developed for these measurements, however, quantitative assessment of the quality of images (micro-nanoparticles, and leukocytes) captured using these devices remains a challenge. Here, we present a novel method for automated quality assessment of fluorescent images (AQAFI) captured using smartphone fluorescence microscopes (SFM). AQAFI utilizes novel feature extraction methods to identify and measure multiple features of interest in leukocyte and micro-nanoparticle images. For validation of AQAFI, fluorescent particles of different diameters (8.3, 2, 1, 0.8 μm) were imaged using custom-designed SFM at a range of excitation voltages (3.8-4.5 V). Particle intensity, particle vicinity intensity, and image background noise were chosen as analytical parameters of interest and measured by the AQAFI algorithm. A control method was developed by manual calculation of these parameters using ImageJ which was subsequently used to validate the performance of the AQAFI method. For micro-nanoparticle images, correlation coefficients with > 0.95 were obtained for each parameter of interest while comparing AQAFI control (ImageJ). Subsequently, key performance indicators (KPIs) , signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) were defined and calculated for these micro-nano particle images using both AQAFI and control methods. Finally, we tested the performance of the AQAFI method on the fluorescent images of human peripheral blood leukocytes captured using our custom SFM. Correlation coefficients of = 0.99 were obtained for each parameter of interest (leukocyte intensity, vicinity intensity, background noise) calculated using AQAFI and control (ImageJ). A high correlation was also found between the CNR and SDNR values calculated using both methods. The developed AQAFI method thus presents an automated and precise way to quantify and assess the quality of fluorescent images (micro-nano particles and leukocytes) captured using portable SFMs. Similarly, this study finds broader applicability and can also be employed with benchtop microscopes for the quantitative assessment of their imaging performance.
微纳米颗粒和白细胞成像在传染病诊断、细胞治疗和生物制造等领域有重要应用。为此已经开发了便携式荧光显微镜，但使用这些设备获取的图像（微纳米颗粒和白细胞）的质量定量评估仍然是一个挑战。在这里，我们提出了一种用于自动评估智能手机荧光显微镜（SFM）拍摄的荧光图像质量的新方法（AQAFI）。AQAFI 利用新的特征提取方法，识别并测量白细胞和微纳米颗粒图像中的多个感兴趣特征。为了验证 AQAFI，使用定制设计的 SFM 在 3.8-4.5V 的一系列激发电压下对不同直径（8.3、2、1、0.8μm）的荧光颗粒进行成像。选择颗粒强度、颗粒附近强度和图像背景噪声作为分析参数，并通过 AQAFI 算法进行测量。开发了一种通过 ImageJ 手动计算这些参数的对照方法，随后用于验证 AQAFI 方法的性能。对于微纳米颗粒图像，对于每个感兴趣参数，AQAFI 与对照（ImageJ）之间的相关系数均 >0.95。随后，使用 AQAFI 和对照方法，为这些微纳米颗粒图像定义并计算了关键性能指标（KPI），即信号噪声比（SDNR）和对比噪声比（CNR）。最后，我们使用我们的定制 SFM 拍摄的人外周血白细胞的荧光图像测试了 AQAFI 方法的性能。对于使用 AQAFI 和对照（ImageJ）计算的每个感兴趣参数（白细胞强度、附近强度、背景噪声），都得到了相关系数 =0.99。使用两种方法计算的 CNR 值之间也存在高度相关性。因此，开发的 AQAFI 方法提供了一种自动且精确的方法，用于量化和评估使用便携式 SFM 拍摄的荧光图像（微纳米颗粒和白细胞）的质量。同样，本研究发现了更广泛的适用性，也可用于台式显微镜，以定量评估其成像性能。
