Providing an accurate count of total leukocytes and specific subsets (such as T-cells and B-cells) within small amounts of whole blood is a rather challenging ordeal due to the lack of techniques that enable the separation of leukocytes from a limited amount of whole blood. In a previous study we designed a microfluidic chip utilizing a micropillar array to isolate T-cells and B-cells from the sub-microliter of whole blood. Due to the variability of cells in size, morphology and color intensity, a Histogram of Oriented Gradients (HOG) based Support Vector Machine (SVM) classifier was proposed with an average accuracy of 94%, specificity of 99% and sensitivity of 90%. The HOG can separate the cells from the background with a high accuracy rate however, some noise is similar in shape and size to the actual cells and this results in misclassification. To alleviate this situation, in this study a convolutional neural network is trained and used to distinguish T-cells and B-cells with an accuracy rate of 98%, a specificity of 99% and a sensitivity of 97%. We also propose an HOG feature based SVM classifier to preselect the detection windows to accelerate the detection to process images in less than 10 min. The proposed on-chip cell detecting and counting method will be useful for numerous applications in diagnosis and for monitoring diseases.

中文翻译：

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使用深度卷积神经网络对T细胞和B细胞进行高精度检测

由于缺乏使白血球从有限量的全血中分离出来的技术，在少量的全血中提供准确的白血球总数和特定子集（例如T细胞和B细胞）的计数是一项颇具挑战性的考验。血液。在先前的研究中，我们设计了一种利用微柱阵列从亚微升全血中分离出T细胞和B细胞的微流控芯片。由于细胞大小，形态和颜色强度的可变性，提出了基于定向梯度直方图（HOG）的支持向量机（SVM）分类器，其平均准确度为94％，特异性为99％，灵敏度为90％。HOG可以以较高的准确率将细胞与背景分离，但是，某些噪声的形状和大小与实际细胞相似，这会导致分类错误。为了缓解这种情况，在本研究中训练了卷积神经网络，并将其用于区分T细胞和B细胞，其准确率为98％，特异性为99％，灵敏度为97％。我们还提出了一种基于HOG功能的SVM分类器，以预先选择检测窗口，以加快检测速度，以在不到10分钟的时间内处理图像。所提出的片上细胞检测和计数方法将对诊断和疾病监测中的许多应用有用。