Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry

Abstract

Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost.

Keywords: Flow cytometry; Hologram; Lens-free imaging; Three-part differential; White blood cell.

Fig. 1

Schematic drawing of the imaging set up. The inset is the detected fluorescent signal for camera triggering.

Fig. 2

Leukocyte subtypes. First row: Cells under conventional microscope. Second row and third row: Reconstructed lens-free cell images.

Fig. 3

Leukocyte recognition pipeline.

Fig. 4

Measurement of cell edge. a: Cell edge and normal lines overlaid on the amplitude image. Green: normal lines of cell edges. Blue line: cell edge obtained from phase image. b: Blue line: intensities of the amplitude located on one of the normal line. Green block: The full width half minimum of the undershoot peak. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

One-way Anova and multiple pairwise comparison on different features. Features are listed in the same order as in Eq. (3). The features of high importance based on LDA analysis are marked and highlighted as red spots. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

Classifiers comparison.

Fig. 7

Confusion matrices for different feature groups.

Fig. 8

Feature selection using LDA. Left column: Cell-type specific weights of all 60 features (features are listed in the same order as in Eq. (3)). First six features with the highest weights are marked. Right column: Cell-type specific features sorted by weights.

Fig. 9

Classification accuracy of selected features.