SIGNIFICANCE: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biops...
Optical cell manipulation has become increasingly valuable in cell-based assays. In this paper, we demonstrate the translational and rotational manipulation of filamentous cells using multiple cooperative microrobots automatically driven by holograph...
An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to id...
Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imag...
The computational power required to classify cell holograms is a major limit to the throughput of label-free cell sorting based on digital holographic microscopy. In this work, a simple integrated photonic stage comprising a collection of silica pill...
We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the...
Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact an...
We propose using a neural network approach in conjunction with digital holographic microscopy in order to rapidly determine relevant parameters such as the core and shell diameter of coated, non-absorbing spheres. We do so without requiring a time-co...
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