A medical information processing apparatus according to an embodiment includes storage circuitry and processing circuitry. The storage circuitry is configured to store therein human body data structured with a plurality of voxels. The processing circuitry is configured to generate human body data by causing each of the voxels in the human body data to store therein composition information based on information about an anatomical structure of a human body, a data value based on medical image data, and relevance to an adjacent voxel.

The invention discloses a multi-threshold segmentation method for medical images based on an improved salp swarm algorithm. A two-dimensional histogram is established by means of a grayscale image of a medical image and a non-local mean image, then a salp swarm algorithm is used to determine thresholds selected by a Kapur entropy-based threshold method, and the salp swarm algorithm is improved and mutated by an individual-linked mutation strategy during the threshold selection process to avoid local optimization, so that the segmentation effect on the medical image is optimized; and the method has the advantages of good robustness and high accuracy.

A data processing method that is suitable for obtaining quantitative information from data obtained by OCT imaging. The image processing method includes acquiring original image data corresponding to a three-dimensional image of a cultured embryo obtained by optical coherence tomography imaging of the embryo and executing a region segmentation the three-dimensional image into a plurality of regions on the basis of the original image data. In the region segmentation, a local thickness calculation is performed on the three-dimensional image to determine an index value indicating a size of an object included in the three-dimensional image, the three-dimensional image is segmented into a region indicated by the index value greater than a predetermined first threshold and a region indicated by the index value less than the first threshold, and each of the regions resulting from the segmentation is further segmented by the watershed algorithm.

A computer-implemented method for processing an electronic image may include receiving, by an artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital whole slide images (WSIs) and extracting one or more vectors of features from one or more foreground tiles of tile images of the one or more digital WSIs. The method may include running a trained machine learning model on the one or more vectors of features and determining, based on an output of the trained machine learning model, whether one or more quality issues are present in the one or more digital WSIs.

Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

An internal prediction method includes acquiring an image of a cell aggregate, calculating a feature amount related to a shape of the cell aggregate on the basis of the image, and outputting structure information related to an internal structure of the cell aggregate on the basis of the feature amount.

The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.

Detecting single bacterial cells in a sample includes collecting, from a sample provided to an imaging apparatus, a multiplicity of images of the sample over a length of time; assessing a trajectory of each bacterial cell in the sample; and assessing, based on the trajectory of each bacterial cell in the sample, a number of bacterial cell divisions that occur in the sample during the length of time.

An image processing device 100 includes, in a case where designation of a plurality of partial regions corresponding to a cell morphology is received, the plurality of partial regions being extracted from a pathological image, a generation unit 154 that generates auxiliary information indicating information about a feature amount effective when a plurality of partial regions is classified or extracted with respect to a plurality of feature amounts calculated from the image; and in a case where setting information about an adjustment item according to the auxiliary information is received, an image processing unit 155 that performs an image process on the image using the setting information.

The present disclosure refers to a computer-implemented training system for user-interactive training of a plurality of in-vitro diagnostic (IVD) methods performable in an IVD laboratory system, comprising: one or more data processors; a memory device connected to the one or more data processors; a user interface provided with an output device having a display device and an input device configured to receive user input; and one or more software applications running on the one or more data processors and having a plurality of application modules. The plurality of application modules is further configured to control, in response to receiving user input, output of a plurality of views of the IVD laboratory system through the display device according to view output control data indicative of view parameters assigned to a view output mode from a plurality of view output modes; receive a training mode selection user input indicative of a user selection for an IVD method to be trained from the plurality of methods having an assigned view output mode of the plurality of view output modes.