The invention provides a technology for promptly determining bacterial identification or an antimicrobial susceptibility testing. In the invention, first, a state where the bacteria are divided is monitored by performing microscopic observation with respect to the shape or the number of bacteria in each of wells of a culture plate for bacterial identification culture or the antimicrobial susceptibility testing. In addition, the shape, the number or the area of the bacteria are interpreted from the image obtained by the microscopic observation whether or not the bacteria proliferate at a stage from an induction phase to a logarithmic phase, and the time-dependent changes thereof are made into a graph. From the graph, it is determined whether or not the bacteria proliferate for each measurement, the determination results are displayed on the screen, and accordingly, the result of the antimicrobial susceptibility is provided every time when the measurement is performed (FIG. 12).

The present technology relates to an image processing device, a method of image processing, and a surgical microscope that can detect and report a dangerous condition on the basis of a tomographic image during eye surgery. An image processing device includes: a dangerous condition detection unit configured to detect a dangerous condition on the basis of a tomographic image of an eye acquired during surgery of the eye; and a control information generation unit configured to generate and output control information used to manage the detected dangerous condition. The present technology is applicable to, for example, a surgical system used for eye surgery or other surgical procedures.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.

Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.

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.

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.

Systems and methods for analysis of a whole blood sample from an individual to determine the platelet function and coagulation status of the individual in a substantially automated and efficient matter. Also provided here are systems, reagent kits, and methods for concurrent assessment of platelet function and coagulation as they interact during hemostasis.

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section (41) that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section (42) that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section (44) that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.

The present disclosure provides methods and systems directed to performing real-time and/or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.