Automatic Classification of Red Blood Cell Morphology Based on Quantitative Phase Imaging

Classification of the morphology of red blood cells (RBCs) plays an extremely important role in evaluating the quality of long-term stored blood, as RBC storage lesions such as transformation of discocytes to echinocytes and then to spherocytes may cause adverse clinical effects. Most RBC segmentation and classification methods, limited by interference of staining procedures and poor details, are based on traditional bright field microscopy. In the present study, quantitative phase imaging (QPI) technology was combined with deep learning for automatic classification of RBC morphology. QPI can be used to observe unstained RBCs with high spatial resolution and phase information. In deep learning based on phase information, boundary curvature is used to reduce inadequate learning for preliminary screening of the three shapes of unstained RBCs. The model accuracy was 97.3% for the stacked sparse autoencoder plus Softmax classifier. Compared with the traditional convolutional neural network, the developed method showed a lower misclassification rate and less processing time, especially for RBCs with more discocytes. This method has potential applications in automatically evaluating the quality of long-term stored blood and real-time diagnosis of RBC-related diseases.