White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).
White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).
复制标题
DOI：
10.1038/s41598-022-21250-z
复制
发表时间：
2022-11-21
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
用赖特染色处理血涂片是检测白细胞异常最有用的工具之一。然而，为了诊断白细胞疾病，临床病理学家必须执行繁琐的手动过程来定位和识别单个细胞。此外，染色过程需要大量的准备时间和临床基础设施，这与即时诊断不相容。因此，非常需要对未标记的血涂片进行快速和自动化的评估。在这项研究中，我们使用彩色空间光干涉显微镜（cSLIM）这种高灵敏度定量相位成像（QPI）技术，结合深度学习工具，对血涂片中的白细胞（WBC）进行定位、分类和分割。最近，在荧光成像领域引入了将 QPI 无标记数据与 AI 相结合以提取细胞特异性的概念，即具有计算特异性的相位成像 (PICS)。我们采用 AI 模型首先将 SLIM 图像转换为明场显微照片，然后使用 EfficientNet（一种对象检测模型）运行定位和标记细胞的并行任务。接下来，使用 U-net 创建 WBC 二进制掩模，U-net 是一种执行精确分割的卷积神经网络。在对白细胞血涂片的数字染色明场图像进行训练后，我们在定位和分类中性粒细胞、嗜酸性粒细胞、淋巴细胞和单核细胞方面实现了 75% 的平均精度，在从语义分割图确定细胞类别方面实现了 80% 的平均像素多数投票 F1 得分。因此，PICS 可快速渲染和分析合成染色的血涂片，降低样品制备成本，提供定量的临床信息。
Treatment of blood smears with Wright’s stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.
登录
查看更多内容
影响因子：
3.7
作者：
Lippeveld, Maxim;Knill, Carly;Peralta, Daniel
通讯作者：
Peralta, Daniel
DOI：
10.1109/tsmc.1972.4309161
发表时间：
1972-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
BACUS, JW;GOSE, EE
通讯作者：
GOSE, EE
层析成像阶段显微镜：生物成像中的原理和应用[邀请]。
DOI：
10.1364/josab.34.000b64
发表时间：
2017
期刊：
Journal of the Optical Society of America. B, Optical physics
影响因子：
--
作者：
Jin D;Zhou R;Yaqoob Z;So PTC
通讯作者：
So PTC
影响因子：
4.6
作者：
Li, Yanfen;Fanous, Michael J.;Popescu, Gabriel
通讯作者：
Popescu, Gabriel
DOI：
10.1109/jstqe.2009.2036741
发表时间：
2010-07-01
影响因子：
4.9
作者：
Di Caprio, Giuseppe;Gioffre, Mariano A.;Coppola, Giuseppe
通讯作者：
Coppola, Giuseppe