Neutrophils are the most abundant circulating white blood cells and one of their critical functions to eliminate pathogenic threats includes the release of extracellular DNA, also known as neutrophil extracellular traps (NETs), which is dysregulated in many diseases including cancer, type 2 diabetes mellitus and infectious diseases. Currently, conventional methods to quantify the NET formation (NETosis) rely on fluorescence antibody-based NET labelling or circulating NET-associated protein detection by ELISA, which are expensive, laborious, and time-consuming. In this work, we employed a novel "virtual staining" using deep convolutional neural networks (CNNs) to facilitate label-free quantification of NETs trapped in a micropillar array in a microfluidic device. Virtual staining is constructed to establish relations between morphological features in phase contrast images and fluorescence features in Sytox-green (DNA dye) images. We first investigated the effect of different learning rates on model training and optimized the learning rate to achieve the best model which can provide outputs close to Sytox green staining based on various reconstruction metrics (e.g., structural similarity (SSIM) and pixel-wise error (MAE, MSE)). The virtual staining of different NET concentrations was investigated which showed a linear correlation with fluorescent staining. As a proof of concept for clinical testing, the model was used to characterize purified neutrophils treated with NETosis inducers, including lipopolysaccharide (LPS), phorbol 12-myristate 13-acetate (PMA), and calcium ionophore (CaI), and successfully detected different NET profiles for different treatments. Collectively, these results demonstrated the potential of using deep learning for enhanced label-free image analysis of NETs for clinical research, drug discovery and point-of-care testing of diseases.