A reconstruction system is presented for reconstructing morphology of biological cells. The system includes data input and output utilities, memory, and a data processor, and is configured for data communication with an image data provider to receive raw image data comprising a sequence of image frames acquired from the cell during a movement of the cell and being indicative of optical path delay (OPD) of light propagation through the biological cell. The data processor is configured and operable to process the raw image data and determine 3D dynamic morphological data of the cell. The data processor includes: a modeling utility; a position recovery module; and an analyzer module.

There is provided a cell evaluation device including an acquisition unit that acquires a cell image in which a nerve cell having a cell body, an axon, and a dendrite is shown; a first specifying unit that specifies the axon in the cell image; a second specifying unit that specifies spines formed on the dendrite in the cell image; a selection unit that selects a synapse-estimated spine, which is estimated to constitute a synapse with the axon and is close to the axon specified in the first specifying unit, from the spines specified in the second specifying unit; and a display control unit that performs control to display the synapse-estimated spine in a display form different from that of other spines in the cell image.

A device, system and method for the detection and screening of plastic microparticles in a sample is disclosed. A nanoporous silicon nitride membrane is used to entrap plastic microparticles contained in the sample. The sample may be a water sample, an air sample, or other liquid or gas sample. The entrapped plastic microparticles are then heated or otherwise processed on the nanoporous silicon nitride membrane. An imaging system observes the nanoporous silicon nitride membrane with tic entrapped plastic microparticles to determine the type and quantity of the various plastic microparticles that are entrapped on the membrane.

A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.

An optical method of characterizing an object comprises providing an object to be characterized, the object having at least one nanoscale feature; illuminating the object with coherent plane wave optical radiation having a wavelength larger than the nanoscale feature; capturing a diffraction intensity pattern of the radiation which is scattered by the object; supplying the diffraction intensity pattern to a neural network trained with a training set of diffraction intensity patterns corresponding to other objects with a same nanoscale feature as the object to be characterized, the neural network configured to recover information about the object from the diffraction intensity pattern; and making a characterization of the object based on the recovered information.

Apparatus and methods are described including acquiring microscopic images of a blood sample, and identifying deformed red blood cells within the microscopic images. A degree of red blood cell deformity within the sample is determined, at least partially based upon the identified deformed red blood cells. A sample-informative parameter that is indicative of a characteristic of the blood sample as a whole is determined, at least partially based upon the degree of red blood cell deformity within the sample. Other applications are also described.

The invention relates to a method for microscopic evaluation (120) of a sample (2), in particular at least one uncolored object or cell sample (2), in an optical detection system (1), where the following steps are performed: providing at least two different detection information (110) about the sample (2), in particular by the detection system (1),
performing an evaluation (120) of the detection information (110), in particular by an analysis means (60), on the basis of machine-learned transfer information (200), in order to determine result information (140) about the sample (2),
the transfer information (200) being trained for a different detection parameterization of the detection information (110), in which the detection information (110) differs from one another in terms of at least one illumination parameter of the detection system (1), in particular in terms of polarization and/or color coding.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

A method and a system of detecting at least one sample in a sample container, comprising a sample container, further comprising a cavity, the volume of said cavity partially or fully occupied with at least one solid sample and at least one fluid; and at least one camera capturing at least one image of the sample container; and a data processing device detecting at least one sample in the sample container by processing the at least one image captured by the at least one camera. The method and system further comprise putting the sample container in sudden motion prior to the at least one camera capturing at least one image of the sample container.

In one aspect, a method to detect white blood cells and/or white blood cell subtypes from non-invasive capillary videos is featured. The method includes acquiring a first plurality of images of a region of interest including one or more capillaries of a predetermined area of a human subject from non-invasive capillary videos captured with an optical device, processing the first plurality of images to determine one or more optical absorption gaps located in said capillary, and annotating the first plurality of images with an indication of any optical absorption gap detected in the first plurality of images. The method also includes acquiring a second plurality of images of the same region of interest of the same capillary with an advanced optical device capable of resolving cellular structure of white blood cells and white blood cell subtypes and spatiotemporally annotating the second plurality of images with an indication of any white blood cell detected and/or a subtype of any white blood cell detected in the second plurality of images. The method also includes inputting the first plurality of images and annotated information from the first plurality of images and annotated information from the spatiotemporally annotated second plurality of images into a machine learning subsystem configured to determine a presence of white blood cells and/or the subtype of any white blood cells present in the one or more optical absorption gaps in the first plurality of images.