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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.

This paper reports a novel on-chip cellular force measurement method of single cells using Direct-Outer-Drive mechanism (D-O-D mechanism). This D-O-D mechanism enables high speed (1 kHz), high power (1 kN) and high accurate manipulation (nanometer-order) of Robochip (Robot-integrated microfluidic chip) and mechanical property measurement of single cells. We succeeded in mechanical property measurement of single animal cells with size of 15 m. Furthermore, by adopting flow systems of microfluidic chip, we can achieve a high-speed, high-precision, and high-throughput consecutive cellular force measurement of single flowing cells.