Apparatus and methods are described for use with a digital microscope unit that includes a digital microscope. A biological cell sample that is disposed within a sample carrier is received into the digital microscope unit. It is determined that there is a variation in the focal depth of the biological sample with respect to the microscope due to curvature in the sample carrier and/or due to tolerance in setup of the microscope. In response to determining that there is the variation in the focal depth of the biological sample with respect to the microscope, the variation in the focal depth of the biological sample with respect to the microscope is accounted for. Other applications are also described.

For analyzing a sample containing particles of at least two categories, such as a sample containing blood cells, a particle counter subject to a detection limit is coupled with an analyzer capable of discerning particle number ratios, such as a visual analyzer, and a processor. A first category of particles can be present beyond detection range limits while a second category of particles is present within respective detection range limits. The concentration of the second category of particles is determined by the particle counter. A ratio of counts of the first category to the second category is determined on the analyzer. The concentration of particles in the first category is calculated on the processor based on the ratio and the count or concentration of particles in the second category.

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

Methods and systems are provided for synchronizing image capture at a multi-detector imaging system. In one example, a method includes coordinating cycling of each microscope assembly of the multi-detector imaging system through a selection of illumination channels, each microscope assembly configured to obtain an image of a portion of one of more than one microplate wells simultaneously, to generate complete images of the more than one microplate wells concurrently.

Disclosed is an optical micro-spectrometry system including an optical microscope, a spectrometry system and an optical system adapted to direct an excitation light beam on the sample through the at least one microscope objective and to collect a Raman or PL light beam from a sample. The optical micro-spectrometry system includes an imaging system configured for acquiring a first image and a second image of the sample, by reflection or transmission of an illumination beam from a sample surface, the first image having a large field of view and the second image having a small field of view, a processing system configured for determining an area in the first image corresponding to the second image, a display system configured for displaying the first image, the second image, and a third image representing the area in overlay on the first image.

A method for determining a focus position includes recording at least one first image, wherein image data of the at least one recorded first image are dependent on at least one first focus position during the recording of the at least one first image. A second focus position is determined based on an analysis of the at least one recorded first image using a trained model. At least one second image is recorded using the second focus position. The at least one first image and the at least one second image contain items of information which are in a context with a training of the trained model.

Disclosed subject matter relates to Peripheral Blood Smear (PBS) that determines an area to be scanned in PBS for analysis. A PBS analysing system captures a focused image at each of plurality of positions in the PBS and determines Quality Indicators (QIs) in focused image. Further, a region is identified in PBS where QIs of focused image satisfy predefined QI threshold limits, as a monolayer region of PBS and determines an initiation point in monolayer region based on cell count value and co-ordinates of each of the plurality of positions located in the monolayer region. Finally, the area to be scanned in monolayer region is determined based on the initiation point and a predefined scan pattern. Determining the area to be scanned yields accurate and faster results.

A microscope system is provided with a microscope that acquires images at least at a first magnification and a second magnification higher than the first magnification, and a processor. The processor is configured to specify a type of a container in which a specimen is placed, and when starting observation of the specimen placed in the container at the second magnification, the processor is configured to specify an observation start position by performing object detection according to the type of container on a first image that includes the container acquired by the microscope at the first magnification, and control a relative position of the microscope with respect to the specimen such that the observation start position is contained in a field of view at the second magnification of the microscope.

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

An apparatus for detecting movements of a sample with respect to an objective comprises imaging optics which include the objective, which have an image plane and which are configured to image light from at least one reference object that is connected to the sample arranged in front of the objective into reference object images in the image plane. The apparatus further comprises a camera which is arranged in the image plane of the imaging optics and which is configured to record the reference object images at consecutive points in time, and an optical device arranged between the objective and the camera in a plane that is Fourier-conjugated with respect to the image plane. The optical device is configured to mask out low spatial frequencies of reference object images which the imaging optics image into the image plane.