Alternation of inverse problem approach and deep learning for lens-free microscopy image reconstruction.

A lens-free microscope is a simple imaging device performing in-line holographic measurements. In the absence of focusing optics, a reconstruction algorithm is used to retrieve the sample image by solving the inverse problem. This is usually performed by optimization algorithms relying on gradient computation. However the presence of local minima leads to unsatisfactory convergence when phase wrapping errors occur. This is particularly the case in large optical thickness samples, for example cells in suspension and cells undergoing mitosis. To date, the occurrence of phase wrapping errors in the holographic reconstruction limits the application of lens-free microscopy in live cell imaging. To overcome this issue, we propose a novel approach in which the reconstruction alternates between two approaches, an inverse problem optimization and deep learning. The computation starts with a first reconstruction guess of the cell sample image. The result is then fed into a neural network, which is trained to correct phase wrapping errors. The neural network prediction is next used as the initialization of a second and last reconstruction step, which corrects to a certain extent the neural network prediction errors. We demonstrate the applicability of this approach in solving the phase wrapping problem occurring with cells in suspension at large densities. This is a challenging sample that typically cannot be reconstructed without phase wrapping errors, when using inverse problem optimization alone.

Multispectral total-variation reconstruction applied to lens-free microscopy.

Lens-free microscopy multispectral acquisitions are processed with an inverse problem approach: a multispectral total variation criterion is defined and minimized with the conjugate gradients method. Reconstruction results show that the method is efficient to recover the phase image of densely packed cells.

Imaging of dense cell cultures by multiwavelength lens-free video microscopy.

They present results for lens-free microscopy for the imaging of dense cell culture. With this aim, they use a multiwavelength LED illumination with well separated wavelengths, together with the implementation of an appropriate holographic reconstruction algorithm. This allows for a fast and efficient reconstruction of the phase image of densely packed cells (up to 700 cells/mm2 ) over a large field of view of 29.4 mm2 . Combined with the compactness of the system which fits altogether inside an incubator, lens-free microscopy becomes a unique tool to monitor cell cultures over several days. The high contrast phase shift images provide robust cell segmentation and tracking, and enable high throughput monitoring of individual cell dimensions, dry mass, and motility. They tested the multiwavelength lens-free video-microscope over a broad range of cell lines, including mesenchymal, endothelial, and epithelial cells. © 2017 International Society for Advancement of Cytometry.

High-throughput monitoring of major cell functions by means of lensfree video microscopy.

Quantification of basic cell functions is a preliminary step to understand complex cellular mechanisms, for e.g., to test compatibility of biomaterials, to assess the effectiveness of drugs and siRNAs, and to control cell behavior. However, commonly used quantification methods are label-dependent, and end-point assays. As an alternative, using our lensfree video microscopy platform to perform high-throughput real-time monitoring of cell culture, we introduce specifically devised metrics that are capable of non-invasive quantification of cell functions such as cell-substrate adhesion, cell spreading, cell division, cell division orientation and cell death. Unlike existing methods, our platform and associated metrics embrace entire population of thousands of cells whilst monitoring the fate of every single cell within the population. This results in a high content description of cell functions that typically contains 25,000 - 900,000 measurements per experiment depending on cell density and period of observation. As proof of concept, we monitored cell-substrate adhesion and spreading kinetics of human Mesenchymal Stem Cells (hMSCs) and primary human fibroblasts, we determined the cell division orientation of hMSCs, and we observed the effect of transfection of siCellDeath (siRNA known to induce cell death) on hMSCs and human Osteo Sarcoma (U2OS) Cells.