Figure 6
From: Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
Deep cytometry: application of deep learning in cell sorting and flow cytometry. A microfluidic channel with hydrodynamic focusing mechanism uses sheath fluid to align the cells in the center of field-of-view. The rainbow pulses formed by the time-stretch imaging system capture line images of the cells in the channel, containing blur-free quantitative label-free images of the cells flowing at a high speed. The output waveforms of the time-stretch imaging system are directly passed to a deep neural network without any signal processing. The network achieves rapid cell classification with high accuracy, fast enough to make decisions before the cells reach the sorting mechanism. Different types of cells are categorized and charged with different polarity charges so that they can be separated into different collection tubes.
