Disclosed is a disease differentiation support method for supporting disease differentiation, the disease differentiation support method including: obtaining a first parameter obtained by analyzing an image including a cell contained in a sample collected from a subject; obtaining a second parameter regarding a number of cells contained in the sample; and generating, by using a computer algorithm, differentiation support information for supporting disease differentiation, on the basis of the first parameter and the second parameter.

A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.

A system comprises a particle sensor unit in communication with a processor. The sensor unit comprises a source that transmits light into an interrogation region; receive optics that collect scattered light from particles in the interrogation region; and an optical detector that receives the collected light from the receive optics. The detector comprises a sample area including one or more sampling pixels, and an edge region including one or more edge pixels. The processor analyzes intensity data from the detector by a method comprising: combining all intensity data from the sampling pixels; adding the combined intensity data to a data set; determining whether to accept overlap intensity data that corresponds to an overlap between the sampling pixels and the edge pixels; adding the overlap intensity data to the data set if accepted; discarding the overlap intensity data if not accepted; and discarding all non-overlapping intensity data from the edge pixels.

A bubble measurement device for measurement of bubbles moving in a liquid includes a measurement chamber having an image capturing surface; an image capturing device that captures an image of the bubbles passing along the image capturing surface; an introduction pipe that introduces the bubbles into the measurement chamber; a retaining tank that stores the liquid; a supply pump that draws up the liquid; a drain pipe that returns the liquid into the retaining tank; and a flow velocity adjusting mechanism that adjusts a flow velocity of the liquid passing along the image capturing surface. The flow velocity adjusting mechanism adjusts the flow velocity of the liquid passing along the image capturing surface to be within a range in which the bubbles are measurable. The range is obtained in advance in accordance with an image resolution and a shutter speed of the image capturing device.

A method for measuring the concentration of a micro/nano particle, including: allowing the to-be-measured micro/nano particle to bind with one or more kinds of marker to form a new particle, the new particle having a change in at least one of particle size, charge state, and particle morphology compared with the to-be-measured micro/nano particle or the marker; measuring the particle size, charge state, or particle morphology of the new particle and the to-be-measured micro/nano particle or the marker, and counting the new particle and the to-be-measured micro/nano particle or the marker respectively to obtain their respective count results, and, on the basis of the count results, calculating the concentration of the to-be-measured micro/nano particle bound with the marker. The method of the present application has the advantages of high measurement accuracy, low measurement limit, and stability of chemical reagents.

An observation device includes an illumination optical system and an observation optical system. The illumination optical system includes a light source and an aperture member. The observation optical system includes an objective lens, an optical structure, and a detector. The optical structure is disposed at a first position which is the position conjugate with the aperture member. The optical structure includes a blocking portion that blocks light and a transmitting portion that transmits light, the blocking portion having a shape including the shape of an image of an aperture of the aperture member which is formed on the optical structure. The detector detects dark-field light passing through the optical structure.

The present disclosure provides apparatuses, systems, and methods for performing particle analysis through flow cytometry at comparatively high event rates and for gathering high resolution images of particles.

In a disclosed example, a computer-implemented method includes storing image data that includes an input image of a blood sample within a blood monitoring device. The method also includes generating, by a machine learning model, a segmentation mask that assigns pixels in the input image to one of a plurality of classes, which correlate to respective known biophysical properties of blood cells. The method also includes extracting cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes.

Systems and methods presented herein generally relate to measuring physical lithological features based on calibrated photographs of cuttings and, more specifically, to the analysis of individual cuttings that are identified in the calibrated photographs of the cuttings. For example, the systems and methods presented herein are configured to receive one or more photographs that depict a plurality of cuttings, to identify one or more individual cuttings of the plurality of cuttings depicted in the one or more photographs, to extract morphological, color, texture, grain size, and grain distribution data from each individual cutting of the one or more individual cuttings, to perform lithological classification of the one or more individual cuttings at a plurality of hierarchical levels based at least in part on the extracted morphological, color, texture, grain size, and grain distribution data or based at least in part on features directly extracted from the one or more individual cuttings that represent the morphological, color, texture, grain size, and grain distribution data, and to present a consolidated results summary of the lithological classification of the one or more individual cuttings at the plurality of hierarchical levels via the analysis and control system.

An airborne biological particle monitoring device collects particles floating in air. The monitoring device includes a processor, a camera sensor, and a set of approximately monochromatic illumination sources that correspond to a set of spectral curves. The camera sensor captures images of the particles providing a spectral analysis of the particles. The processor analyzes the images to identify the collected particles.