A system for analyzing biological specimens by spectral imaging includes a biosensor comprising at least one graphene layer on a substrate and a memory in communication with a processor. The biosensor is configured to acquire a biological specimen sample. The memory and the processor are configured to conduct Raman spectroscopy to obtain spectral data for the sample, transmit the spectral data to a hub for direct or indirect transmission to one or more servers, perform multivariate analysis on the spectral data, and deliver a report based on the multivariate analysis of the spectral data.

A method and system are provided for illuminating and imaging a biological sample using a brightfield microscope for the purpose of counting biological cells. The method comprises positioning a sample to be viewed by way of an objective lens of the microscope, the sample comprising a plurality of biological cells; capturing and storing, using an image capturing apparatus, one or more focal image stacks; processing the one or more focal image stacks using a cell localisation neural network, the cell localisation neural network outputting a list of one or more cell locations; determining, using the list of cell locations, one or more cell focal image stacks, each cell focal image stack being obtained from the one or more focal image stacks; processing the one or more cell focal image stacks using an encoder neural network; determining, using the list of cell locations and the list of cell fingerprints, a number of cells within the sample. The present disclosure aims to provide a quick, non-invasive and reliable mode of counting biological cells.

An optical measurement system comprises a polarization beam splitter for dividing an incident beam into a reference beam and a measurement beam, a first beam splitter for reflecting the measurement beam to form a first reflected measurement beam, a spatial light modulator for modulating the first reflected measurement beam to form a modulated measurement beam, a condenser lens for focusing the modulated measurement beam to an object to form a penetrating measurement beam, an objective lens for converting the penetrating measurement beam into a parallel measurement beam, a mirror for reflecting the parallel measurement beam to form an object beam, a second beam splitter for reflecting the reference beam to a path coincident with that of the object beam, and a camera for receiving an interference signal generated by the reference beam and the object beam to generate an image of the object.

A urine analysis system according to an embodiment includes: a testing apparatus that measures particles included in a urine sample according to a flow cytometry method; an image capturing apparatus that captures images of particles in the urine sample to acquire particle images; and a management apparatus that receives a measurement result obtained by the testing apparatus and the particle images acquired by the image capturing apparatus. The management apparatus generates an order to capture an image of the urine sample based on the measurement result obtained by the testing apparatus. The image capturing apparatus executes the image capturing processing of the particles in the urine sample for which the image capturing order has been generated by the management apparatus, and transmits the acquired particle images to the management apparatus.

Optical biopsy staining panels for in vivo or in situ fluorescent staining of optical tissue (or other appropriate tissue), e.g., for the purpose of a direct biopsy such as an optical biopsy. The stain panels may feature a combination of a nuclear stain and a cytoplasmic stain, as a means of functioning as an invivo or in situ hematoxylin and eosin (H&E) stain. Examples of said stains may include anthracyclines such as Daunomcibin, acriflavines like Proflavine, anthracenediones such as Mitoxantrone, phenothiazines like Methylene Blue, and tri- and tetra-heterocyclic dyes like Fluorescein, Phloxine B, Phenol Red, Rose Bengal, Congo Red, and Indigo Carmine.

A computer-implemented method comprises the following steps. In one step an image is acquired which is captured in the context of a microscopy measurement and images a sample to be examined. In one step the microscopy measurement is monitored in an automated manner. On the basis of the automated monitoring of the microscopy measurement, one or more labels are created, wherein said one or more labels comprise semantic context information of the microscopy measurement. On the basis of the image as input and said one or more labels as ground truth, a machine-learned algorithm is trained which provides semantic context information on the basis of images captured in the context of microscopy measurements. In a further step a further image is acquired, which is captured in the context of the microscopy measurement or a further microscopy measurement by the microscope and images the sample or a further sample. In a further step the trained machine-learned algorithm is applied to the further image in order to predict further semantic context information for the further image.

A deep-ultraviolet microscopy system includes a light source for outputting a light beam for illuminating a biological sample, the light beam being inclusive of ultraviolet wavelengths; a reception space for reception of a biological sample for illumination by the light beam; an ultraviolet microscope objective for collecting and relaying light that interacts with the biological sample to an image capture device; and an ultraviolet sensitive image capture device for capturing images of the biological sample, with the microscopy system configured to capture multiple images of the biological sample at one or more ultraviolet wavelengths. A method of processing ultraviolet images of biological samples includes receiving a plurality of multi-spectral ultraviolet images of a biological sample; normalizing and scaling the images; and assigning each image to a channel in the RGB color-space based on wavelength.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Methods, devices, apparatus, and systems for three-dimensional (3D) contoured scanning photoacoustic imaging and/or deep learning virtual staining.

A mobile phone-based miniature microscopic image acquisition device, and image stitching and recognition methods are provided. The acquisition device comprises a support, wherein a mobile phone fixing table is provided on the support. A microscope head is provided below a camera of a mobile phone. A slide holder is provided below the microscope head, and an lighting source is provided below the slide holder. A scanning movement is performed between the slide holder and the microscope head along X and Y axes, so that images of a slide are acquired into the mobile phone. The slide sample images acquired into the mobile phone can be stitched and recognized, and can be uploaded to the cloud to be processed by cloud AI, thereby significantly improving the accuracy and efficiency of cell recognition, greatly reducing the medical cost, and ensuring more remote medical institutions can apply such technology for diagnosis.