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.
The harmful effects of Cryptosporidium oocysts and Giardia cysts in drinking water have been widely concerned by the international community. Currently, the EPA1623 method is one of the most mature and authoritative methods for detecting Cryptosporidium oocysts and Giardia cysts internationally. However, this method has the limitations of high cost of time and human labor. Based on ultrashort pulse time-space-frequency mapping principle, ultrafast time-encoded flow imaging can reach high speed and high resolution. Therefore, it is proposed for replacing the last three steps of EPA1623, which are immunomagnetic separation, fluorescent staining and enumeration. Specifically, mixed with immunomagnetic beads, the liquid quality sample of Giardia cysts and Cryptosporidium oocysts flow through the microfluidic channel with high throughput of 100 particles/s. With ultrafast time-encoded flow imaging system, images are acquired including oocysts and cysts which are magnetized by attachment of magnetic beads or not, and only magnetic beads. Extracted appearance and shape features, images are classified by K-means cluster algorithm. It is shown in results that, ultrafast time-encoded flow imaging method costs less than 10 minutes and maintains recovery at more than 80%, compared to the last three steps in EPA1623 which need almost 2 hours at less recovery. The proposed method makes full use of the biological properties of immunomagnetic beads, Cryptosporidium oocysts and Giardia cysts, and maintains high percent recovery with much shorter detection time.
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