Aspects of the present disclosure include systems for detecting light from a particle by birefringent interferometry. Systems according to certain embodiments include a light source configured to irradiate a particle propagating through a flow stream, a light detection system that includes a birefringent polarizing interferometer configured to generate interfering polarized beams of light, a light adjustment component configured to continuously convey light from the irradiated particle across different positions on the birefringent polarizing interferometer as the particle is propagated through the flow stream, a photodetector configured to detect interference patterns of the interfering polarized beams of light generated by the birefringent polarizing interferometer from light collected from the irradiated particle and generate a photodetector signal pulse in response to each detected interference pattern. Systems also include a processor for transforming the photodetector signal pulses into spectral data signals. Methods for detecting light with the subject systems are also described. Kits having one or more components for detecting light according to the subject methods are also provided.

An imaging flow cytometer device includes a housing holding a multi-color illumination source configured for pulsed or continuous wave operation. A microfluidic channel is disposed in the housing and is fluidically coupled to a source of fluid containing objects that flow through the microfluidic channel. A color image sensor is disposed adjacent to the microfluidic channel and receives light from the illumination source that passes through the microfluidic channel. The image sensor captures image frames containing raw hologram images of the moving objects passing through the microfluidic channel. The image frames are subject to image processing to reconstruct phase and/or intensity images of the moving objects for each color. The reconstructed phase and/or intensity images are then input to a trained deep neural network that outputs a phase recovered image of the moving objects. The trained deep neural network may also be trained to classify object types.

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.

Embodiments of the present disclosure provide for improved generation and outputting of object identification data indicating object classifications for object representations. Such objects representations may correspond to depictions of objects in images captured using digital holographic microscopy. Some embodiments generate object identification data by comparing object representations in focused image(s) with specially configured annotated focused images, for example using a specially trained neural network or other machine learning model trained based on such annotated focused images. The annotated focused images are generated including a plurality of channels, each associated with a different grayscale focused image at a different target focal length of a range of target focal lengths. In this regard, model(s), algorithm(s), and/or other specially configured implementations may learn the spatial features of particular object representations and associated object identification data. The trained models may be used to perform accurate comparisons with the annotated focused images.

An information processing apparatus that executes processing of obtaining, from an interference fringe image that is a two-dimensional distribution of intensity of interference fringes of object light and reference light, a phase difference image that is a two-dimensional distribution of a phase difference between the object light and the reference light, and obtaining a shape of an object to be observed based on the phase difference image includes at least one processor configured to acquire object-related information regarding the object to be observed, read out, from a storage unit in which the object-related information and a shape profile indicating the shape of the object to be observed are stored in association with each other, the shape profile corresponding to the acquired object-related information, and perform phase connection with respect to the phase difference image with reference to the read-out shape profile.

A cell area extraction unit (241) extracts a cell area in a phase image that is created based on a hologram obtained by in-line holographic microscope (IHM). A background value acquisition unit (242) obtains a background value from phase values at a plurality of positions outside the cell area. An intracellular phase value acquisition unit (243) averages a plurality of phase values on a sampling line set at a position close to the periphery of a cell, while avoiding a central portion in which the phase value may be lowered in the cell area, to obtain an intracellular phase value. A phase change amount calculation unit (244) obtains the difference between the intracellular phase value and the background value. A phase change amount determination unit (245) compares the value of the difference with thresholds in two levels to determine whether the cell is in an undifferentiated state or an undifferentiation deviant state. It is thereby possible to automatically make a correct determination while removing the influence of a theoretical measurement error by IHM.

A device for detecting particles in air; said device comprising: a flow channel configured to allow a flow of air comprising particles through the flow channel; a light source configured to illuminate the particles, such that an interference pattern is formed by interference between light being scattered by the particles and non-scattered light from the light source; an image sensor configured to detect incident light, detect the interference pattern, and to acquire a time-sequence of image frames, each image frame comprising a plurality of pixels, each pixel representing a detected intensity of light; and a frame processor configured to filter information in the time-sequence of image frames, wherein said filtering comprises:
identifying pixels of interest in the time-sequence of image frames, said pixels of interest picturing an interference pattern potentially representing a particle in the flow of air, and outputting said identified pixels of interest for performing digital holographic reconstruction.

Provided are an imaging system and an imaging device capable of generating a super-resolution interference fringe image of an object to be observed flowing through a flow channel. A light source that irradiates light in a first direction and irradiates light toward a flow channel through which an object to be observed flows in a second direction orthogonal to the first direction, an imaging sensor that has an imaging surface orthogonal to the first direction and on which a plurality of pixels are two-dimensionally arranged in a manner non-parallel to the second direction and that images light passing through the flow channel to output an interference fringe image, and an information processing device that generates a super-resolution interference fringe image based on a plurality of interference fringe images output from the imaging sensor are included.

An interference image acquisition apparatus includes a light source, a beam splitter, a second reflection mirror, an imager, and a first reflection mirror. A cell is placed on one side of a transparent material, and the first reflection mirror is placed on the other side of the transparent material. In a two-beam interferometer, an optical path difference between an optical path length of a first light beam reflected by the first reflection mirror and an optical path length of a second light beam reflected by the second reflection mirror is set to a coherence length of light output from the light source or less. The imager acquires an interference image in a state in which the cell is placed at a position conjugate to an imaging plane in a first optical system between the imaging plane and the first reflection mirror.

A method of training AI for label-free cell viability determination includes a step of providing a cell sample, a step of obtaining a fluorescence image and a DHM image of the cell sample, a step of determining a first cell viability of the cell sample according to the fluorescence image of the cell sample, a step of labeling the DHM image of the cell sample as a model specifying the first cell viability, and a step of performing AI training by using the model containing the DHM image of the cell sample.