Significance: The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed.

Aim: We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification.

Approach: We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope.

Results: The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000  cells  /  s. In addition, we determined the optimal structure of training sets according to model performances under different training set components.

Conclusions: The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.