Marquez Luis A Pena, Chakrabarty Subhajit
Computer Science Louisiana State University Shreveport Shreveport, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022 Dec;2022:2656-2659. doi: 10.1109/bibm55620.2022.9994922.
The classification of white cells plays an important part in medical diagnosis. The counts may suggest the presence of infection, inflammation, anemia, bleeding, and other blood-associated issues. More specifically, the counting in our study is the calculation of the Monocyte Index (MI). The purpose of MI is to determine whether the patient can receive units of blood by analyzing the assay. In case of incompatible blood transfusion, monocytes may ingest or adhere red cells. The index is the percentage of red cells adhered, ingested, or both, versus free monocytes. Manual methods for blood cell counting may take several hours and are highly prone to different sources of errors. Automatic methods, such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Convolutional Neural Network (CNN), Fast Region-based CNN, Faster Region-based CNN, Spatial Pyramidal Pooling network, Single Shot Detector and Mask Region-based CNN, exist for classification. However, these methods currently do not perform automatic counting and calculation of MI. The dataset is our own collection of images using ZEISS Axiocam 208 color/202 mono microscope camera. For the labels in our own collection, we performed polygonal annotation using the VGG Annotator tool. We trained the Mask R-CNN deep neural network model for automatic segmentation at the pixel-level, using COCO pre-trained weights. Our results look promising, as the Mask R-CNN can perform automatic segmentation with 72% accuracy. Compared to a medical laboratory scientist, the model can process large amount of data simultaneously, quickly and efficiently, with approximately the same judgment accuracy as a human eye. This may significantly reduce the burden of the laboratory scientist and provide a useful reference for doctors to identify a potential blood candidate to be transfused.
白细胞分类在医学诊断中起着重要作用。细胞计数可能提示感染、炎症、贫血、出血及其他血液相关问题。更具体地说，我们研究中的计数是单核细胞指数（MI）的计算。MI的目的是通过分析检测来确定患者是否可以接受输血。在输血不兼容的情况下，单核细胞可能会吞噬或黏附红细胞。该指数是黏附、吞噬或两者兼有的红细胞与游离单核细胞的百分比。血细胞计数的手工方法可能需要数小时，并且极易出现各种误差来源。存在用于分类的自动方法，如线性判别分析、二次判别分析、K近邻算法、朴素贝叶斯、支持向量机、卷积神经网络（CNN）、快速区域卷积神经网络、更快区域卷积神经网络、空间金字塔池化网络、单发检测器和基于掩码区域的卷积神经网络。然而，这些方法目前无法自动计数和计算MI。数据集是我们自己使用蔡司Axiocam 208彩色/202单色显微镜相机收集的图像。对于我们自己收集的图像标签，我们使用VGG注释工具进行多边形注释。我们使用COCO预训练权重训练了Mask R-CNN深度神经网络模型，用于像素级的自动分割。我们的结果看起来很有前景，因为Mask R-CNN可以以72%的准确率进行自动分割。与医学检验科学家相比，该模型可以同时快速有效地处理大量数据，判断准确率与肉眼大致相同。这可能会显著减轻检验科学家的负担，并为医生识别潜在的输血候选者提供有用的参考。
