The first aspect of the present invention provides a migration ability evaluation method comprising: a trajectory generation step of generating a trajectory of movement of an object being observed of a living body on the basis of a plurality of images of the object being observed acquired by capturing observation images of the object being observed multiple times in a time-series manner; a migration ability calculation step of calculating a migration ability measure indicating the degree of migration of the object being observed in a certain direction on the basis of the trajectory of movement of the object being observed; and a migration ability evaluation step of evaluating whether or not the object being observed satisfies a predetermined condition on the basis of the migration ability measure of the object being observed.

This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.

A method (400) and a system (200) for generating a chromatically modified image of one or more components on a microscopic slide (303) is disclosed. In one aspect of the invention, the method includes obtaining the image of the one or more components on the microscopic slide (303). Additionally, the method (400) includes processing the image to identify the one or more components. The method (400) further includes segmenting at least one part of the one or more components identified from the image. Furthermore, the method (400) includes chromatically modifying the at least one part of the one or more components and generating a chromatically modified image of the one or more components.

A computer-implemented system and method for predicting female sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.

The invention relates to a method in the preparation of samples for microscopic examination onto which a coverslip is applied. The method is notable for the fact that the coverslipping quality is checked automatically and at least partly optically. The invention further relates to an apparatus for carrying out the method, and to an apparatus for checking the coverslipping quality of samples onto which a coverslip is applied.

The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification. For example, by combining both the static features of the cell along with information on the cell motility, classification can be performed to determine the type of cell present in addition to the state of the cell (e.g., diseased vs. healthy).

The invention relates to a method for operating a stereoscopic medical microscope, wherein deteriorated and/or invalid calibration data are recognized, wherein for this purpose mutually corresponding image representations of at least one feature arranged in capture regions of cameras of a stereo camera system of the medical microscope are captured by means of the cameras, the captured image representations are evaluated by means of feature-based image processing, wherein the at least one feature is recognized in this case in the captured image representations and a misalignment and/or a decalibration of the cameras of the stereo camera system are/is recognized on the basis of the at least one feature recognized; and wherein at least one measure is carried out depending on an evaluation result. Furthermore, the invention relates to a medical microscope.

Apparatus and methods are described including analyzing one or more microscopic images of the blood sample using a machine-learning classifier. An entity within the one or more microscopic images is identified using a first classification model, and a first estimated concentration of the entity within the sample is determined, based upon the entity as identified using the first classification model. The entity is identified within the one or more microscopic images using a second classification model, and a second estimated concentration of the entity within the sample is determined, based upon the entity as identified using the second classification model. The first and second estimated concentrations are compared to each other, and, in response to the comparison, a hybrid classification model that is a hybrid of the first and second classification models is used. Other applications are also described.

The invention relates to an aqueous solution of AuCl.sub.3×2H.sub.2O or HAuCl.sub.4×4H.sub.2O, ZnCl.sub.2 or ZnSO.sub.4, and AgNO.sub.3 having an Au.sup.3+ concentration of 0.8 mM-1.6 mM, a Zn.sup.2+ concentration of 15 μM-50 μM, and an Ag.sup.+ concentration of 5 μM-50 μM. The invention extends to a kit comprising a solution of the invention, a method of preparing said solution, and the use of a solution of the invention, a solution prepared by a method of the invention, or a kit of the invention in predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum. The invention extends to a method for predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum in a patient comprising contacting a tear sample from the patient normalised for protein concentration with a solution of the invention; applying an amount of the tear sample thus obtained to a surface; allowing the tear sample to dry; and drawing a conclusion based on the pattern of the thus obtained dried tear sample, whether the patient is at risk of or suffers from a neurocognitive disorder of the Alzheimer's disease spectrum.