A method for characterizing particles producing heat when exposed to light. The method includes the steps of stimulating a particle sample alternatingly with homogenous light waves with at least a first wavelength and a second wavelength, detecting by a detector heat radiated by the particle sample as a result of the stimulation, thereby yielding time-dependent images of a modulated heat distribution pattern, converting the time-dependent image of the modulated heat distribution pattern into the frequency domain and demodulating the image of the modulated heat distribution pattern, and determining a physical property of the particle sample based on the at least one demodulated image of heat distribution.

A method comprises obtaining input data comprising a hologram of a 3-dimensional (3D) particle field, a depth map of the 3D particle field, and a maximum phase projection of the 3D particle field. The method also comprises applying a U-net convolutional neural network (CNN) to the input data to generate output data. Encoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the encoder block. Decoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the decoder block. The output data includes a channel in which pixel intensity corresponds to relative depth of particles in the 3D particle field and an output image indicating locations of centroids of the particles in the 3D particle field.

A method for pre-scanning a transparent cell culture plate with a plurality of wells to improve focus using a plurality of z-axis images and a method for focusing an optical system on a transparent cell culture plate by performing a Fourier transform on image data at different focus distance steps to reveal a pattern and using the pattern at each step to determine the focus distance.

The inventive concept relates to a device for detecting particles in air, said device comprising a receiver for receiving a flow of air comprising particles, a sample carrier, and a particle capturing arrangement. The particle capturing arrangement is configured to separate the particles from the flow of air for and to collect a set of particles on a surface of the sample carrier. The device further comprises a light source configured to illuminate the particles on the sample carrier, such that an interference pattern is formed by interference between light being scattered by the particles and non-scattered light from the light source. The device further comprises an image sensor configured to detect the interference pattern. The device further comprises a cleaner configured for cleaning the surface of the sample carrier for enabling re-use of the surface for collection of a subsequent set of particles.

A holographic microscopy characterization (HMC) process for utilizing holographic video microscopy to provide an efficient, automated, label-free method of accurately identifying cell viability. Optical properties of a sample of cells are determined by HMC. The optical properties are compared to known samples or compared over time to observe changes in the optical properties, enabling identification of cells as viable or not viable, or as extra-cellular or degraded cellular materials.

The invention relates to a method for detecting particles, having the steps of: receiving (S1) a measurement signal; calculating (S2) at least one estimated noise value using the received measurement signal; and detecting (S3) the particles using the measurement signal on the basis of at least one detection criterion, wherein the at least one detection criterion depends on the at least one calculated estimated noise value.

Provided are a morphological cell parameter-based erythrocyte test method and digital holographic microscope used therein, and the morphological cell parameter-based erythrocyte test method includes performing modeling to create a 3D image of an erythrocyte to be tested and measuring morphological parameters of the erythrocyte based on the 3D image.

The morphological cell parameter-based erythrocyte test method performs modeling of a 3D image for an erythrocyte to be tested and measures morphological parameters of the erythrocyte based on the 3D image. Therefore, time and effort consumed in measurement may be reduced, and accuracy of the measurement is excellent.

Disclosed is a method of measuring red blood cell membrane fluctuations based on dynamic cell parameters using a digital holographic microscope; the method including a step of modeling the three-dimensional images of red blood cells to be measured, and a step of measuring red blood cell membrane fluctuations based on the three-dimensional images. According to this method, since the three-dimensional images of red blood cells to be measured are modeled and red blood cell membrane fluctuations are measured based on the three-dimensional images, red blood cell membrane fluctuations can be measured more easily.

Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.

A method of imaging a sample by interferometric scattering microscopy, the method comprising illuminating a sample with at least one light source, the sample being held at a sample location comprising a reflective surface, such that a reflected signal is formed; the reflected signal comprising light from the light source and light scattered by the sample; detecting the output light over a first time window for a first frame N.sub.1; detecting the output light over a second time window for a second frame N.sub.2; calculating a ratiometric signal R which is the ratio of N.sub.1 and N.sub.2 minus 1; estimating the ratiometric motion signature S=(S.sub.x, S.sub.y) from frames N.sub.1 and N.sub.2 with S defined as the ratiometric image which would be measured from an invariant sample moving along x and y for a given motion vector m=(m.sub.x, m.sub.y); estimating m as the most consistent vector such that R is approximated using S and m; calculating the corrected ratiometric contrast image R* from R, S and m.