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.
Optofluidic time-stretch imaging system has enabled high-throughput phenotyping of cells with unprecedented high speed and resolution. However, significant amount of raw image data is produced, which requires recognition algorithm with not only high accuracy but also high speed to analyze image data efficiently. In this paper, we compare the performance of popular feature extraction methods and learning-based classification algorithms on time-stretch microscopy image recognition. The applied image recognition system comprises an outlier detection step, feature extraction method and classification. The main concept of outlier detection uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to eliminate error images. Gabor wavelet, HOG (Histograms of Oriented Gradients), LBP (Local Binary Pattern) and PCA (Principal Components Analysis) are applied and compared as the feature extraction methods. Finally, with a set of extracted features, the computing time and accuracy of SVM (Support Vector Machines), LR (Logistics Regression), ResNet (Residual Neural Network) and XGBoost (Extreme Gradient Boosting) classification algorithms are evaluated. The tested cell image datasets are acquired from high-throughput imaging of numerous drug-treated and untreated cells (N = ~21,000) with an optofluidic time-stretch microscope. Results show that PCA feature extraction and XGBoost classification proves to be the fastest algorithms with the highest level of accuracy. DBSCAN outlier detection helps to improve the recognition accuracy by 2% approximately. Therefore, we propose a recognition algorithm consisting of DBSCAN outlier detection, PCA feature extraction and XGBoost classification as a promising solution to process the image data of high-throughput optofluidic time-stretch microscopy accurately and rapidly.
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.
KEYWORDS: Ultrafast imaging, Flow cytometry, Imaging systems, Signal detection, Signal processing, Field programmable gate arrays, Data acquisition, Data storage, Signal generators, Pulsed laser operation
Ultrafast imaging flow cytometry can be realized by time-encoded single-pixel imaging technique, with high imaging speed (<10million frame/s) and high throughput (<10,000 cells/s). However, the signal of background image without cells occupies a large part of the acquired data and takes up a lot of storage space. In this paper, a FPGA-based triggering and storage system is proposed, which allows real-time storage of signal of cells with blank background neglected. Moreover, it is easy to implement and of high accuracy, as well as adaptivity to different sampling rate. This system reduces the required storage space and enables efficient storage for ultrafast imaging flow cytometry.
An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.
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