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.

A method and/or system can include processing a blood sample of a patient by degrading red blood cells of the blood sample using a lysing solution, quenching the degradation of the red blood cells after a threshold lysing time, centrifuging and aspirating the quenched solution to remove degraded red blood cell debris and concentrate white blood cells of the blood sample, and suspending the concentrated white blood cells in a buffer solution; within a threshold transfer time, deforming white blood cells, of the suspended white blood cells, within a microfluidic chip; and determining a probability that the patient is in an immune activation state based on images of the white blood cells acquired while deforming the white blood cells.

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.

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.

Methods and systems for sorting particles are provided. Methods and systems for sorting cell beads are provided. In some cases, cell beads may be sorted from particles unoccupied with cell derivatives. In some cases, singularly occupied cell beads may be sorted from unoccupied particles and multiply occupied cell beads.

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.

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.

Aspects of the present disclosure include a particle sorting module having an opening that is configured for visualizing droplets of a deflected flow stream. Particle sorting modules according to certain embodiments include a housing having a proximal end, a distal end and a wall therebetween having an opening positioned in the wall that is configured for aligning the flow stream with one or more sample containers at the distal end. Systems and methods for aligning a flow stream with one or more sample containers and sorting particles of a sample (e.g., a biological sample) are also provided. Kits having one or more of the particle sorting modules suitable for coupling with a particle sorting system and for practicing the subject methods are also described.

The invention provides devices and methods for linked multimodal measurements of individual particles using a mass sensor and an additional sensor.