Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

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.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

Systems and methods are described for the fully automatic, template-free locating and extracting of a plurality of two-dimensional projections of particles in a micrograph image. A set of reference images is automatically assembled from a micrograph image by analyzing the image data in each of a plurality of partially overlapping windows and identifying a subset of windows with image data satisfying at least one statistic criterion compared to other windows. A normalized cross-correlation is then calculated between the image data in each reference image and the image data in each of a plurality of query image windows. Based on this cross-correlation analysis, a plurality of locations in the micrograph is automatically identified as containing a two-dimensional projection of a different instance of the particle of the first type. The two-dimensional projections identified in the micrograph are then used to determine the three-dimensional structure of the particle.

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.

An embodiment of a method 100 for predicting a future state of a biological system is provided. The method 100 comprises receiving 101a microscope image depicting the biological system at an associated time and receiving 102 metadata corresponding to the microscope image. The method 100 further comprises extracting 103 features from the microscope image having information on a state of the biological system and using 104 the features and the metadata to predict the future state of the biological system.

Methods, devices, and systems for improving the quality of electrospray ionization mass spectrometer (ESI-MS) data are described, as are methods, devices, and systems for achieving improved correlation between chemical separation data and mass spectrometry data.

A system for identifying microbial agents such as virus particles in a sample. The system includes at least one processing unit for identifying in an electron micrograph obtained from the sample a darker region and identifying virus particles within the darker region. The system can optionally include an electron microscope, a sample collector and sample treatment chamber.

This disclosure describes single-use test cartridges, cell analyzer apparatus, and methods for automatically performing microscopic cell analysis tasks, such as counting and analyzing blood cells in biological samples. A small measured quantity of a biological sample, such as whole blood, is placed in a mixing bowl on the disposable test cartridge after being inserted into the cell analyzer. The analayzer also deposits a known amount of diluent/stain in the mixing bowl and mixes it with the blood. The analyzer takes a measured amount of the mixture and dispenses in a sample cup on the cartridge in fluid communication with an imaging chamber. The geometry of the imaging chamber is chosen to maintain the uniformity of the mixture, and to prevent cells from crowding or clumping as it is transferred into the imaging chamber by the analyzer. Images of all of the cellular components within the imaging chamber are counted and analyzed to obtain a complete blood count.

A system and method for analyzing bodily fluid include a sample holder holding a bodily fluid sample, an image capture device generating an image of the bodily fluid sample comprising a plurality of fields of view. An image processor is programmed to determine a biofilm in the bodily fluid sample from the image, determine a biofilm area or volume within each of the plurality of fields of view to form a plurality of biofilm areas, determine a total biofilm area or total biofilm volume by adding the plurality of biofilm areas, determine a first value corresponding to a comparison of the total biofilm area or the total biofilm volume and a total volume of the bodily fluid sample, and classify the first value into a classification. An analyzer, using the classification, displays an indicator on a display for indicating the classification of the biofilm within the bodily fluid sample.