A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

In one aspect, the present teachings provide a system for performing cytometry that can be operated in three operational modes. In one operational mode, a fluorescence image of a sample is obtained by exciting one or more fluorophore(s) present in the sample by an excitation beam formed as a superposition of a top-hat-shaped beam with a plurality of beams that are radiofrequency shifted relative to one another. In another operational mode, a sample can be illuminated successively over a time interval by a laser beam at a plurality of excitation frequencies in a scanning fashion. The fluorescence emission from the sample can be detected and analyzed, e.g., to generate a fluorescence image of the sample. In yet another operational mode, the system can be operated to illuminate a plurality of locations of a sample concurrently by a single excitation frequency, which can be generated, e.g., by shifting the central frequency of a laser beam by a radiofrequency. For example, a horizontal extent of the sample can be illuminated by a laser beam at a single excitation frequency. The detected fluorescence radiation can be used to analyze the fluorescence content of the sample, e.g., a cell/particle.

Techniques in connection with the use of a multi-pixel polarization filter in the light-microscopic examination of a sample object are described. In this way e.g. a particle analysis can be carried out, e.g. in particular for determining the technical cleanness of a surface of the sample object.

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

A device (100) for visualization of one or more components in a blood sample is disclosed. In one aspect, the device (100) includes an imaging module (110), wherein the imaging module (110) includes a controllable illumination source (102) capable of emitting light in plurality of discrete angles; a tube lens (105); one or more objective lens (104); and an image capturing module (106). Additionally, the device (100) includes a channel (103) configured to carry the blood sample, wherein the channel (103) is capable of sorting the one or more components in the blood sample.

A method of automated measurement of motility and morphology parameters of the same single motile sperm. Automated motility and morphology measurements of the same single sperm are performed under different microscope magnifications. The same single motile sperm is automatically positioned and kept inside microscope field of view and in focus after magnification switch. A method of automated non-invasive measurement of sperm morphology parameters under high magnification of imaging. Sperm morphology parameters including subcellular structures are automatically measured without invasive sample staining. A method of automatically selecting sperms with normal motility and morphology and DNA integrity for infertility treatment.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

In a bubble measurement device for measuring bubbles moving in a liquid, the bubble measurement device includes a measurement chamber in which the bubbles in the liquid containing solid materials are introduced into the measurement chamber from below the measurement chamber, and providing a transparent slope facing diagonally downward at a position where the introduced bubbles rise, an image capturing device to capture an image of the bubbles passing the transparent slope, an introduction pipe provided below the measurement chamber to introduce the bubbles into the measurement chamber, and a bubble introduction valve that is immersed in the liquid to be measured and performs the introduction and blocking of the bubbles into the introduction pipe.

Provided are a sample image analyzer and a corresponding method. The sample image analyzer includes: an object stage for supporting a sample carrier; an imaging device for capturing an image of an object in a sample on the sample carrier; a driving device for driving the object stage and the imaging device to move relative to each other; and a control device configured to control the driving device to deliver the sample carrier to a position below the imaging device, control the driving device to drive the object stage and the imaging device to move horizontally relative to each other, and to move vertically relative to each other, control the imaging device to capture, at least during the relative vertical movement, images of the object at different horizontal positions and at different vertical positions, and fuse the images of the object to obtain a target image of the object.

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.