The present disclosure provides methods for automated slide assessments made in conjunction with digital image-based microscopy. Automated methods of acquiring patient information and specimen information from prepared slides, and digitally linking such information into patient-tagged specimen data, are provided. Also provided are methods that include automatically identifying an optimal area for morphological assessment of a blood smear on a hematological slide, including methods for triggering the analysis of such an area, e.g., using an automated digital image-based hematology system. The present disclosure also provides devices, systems and computer readable media for use in performing processes of the herein described methods.

In relation to application of artificial intelligence to image analysis of particles, to make it possible to provide data for machine learning corresponding to user demands while making it possible to reduce, as much as possible, man-hours taken to, for example, prepare vast amounts of actual image data obtained by actually capturing images of particles, the present invention generates virtual particle image data, which is image data of a virtual particle, on the basis of a predetermined condition, generates label data corresponding to the virtual particle, and associates the virtual particle image data with the label data.

The invention relates to a method for automatically examining a liquid sample, which has a liquid and at least one particle, by means of a first measurement signal which emanates from a liquid region and which serves to determine at least one particle with a first property, and by means of a second measurement signal which emanates from the liquid region and serves to determine at least one particle with a second property, the first measurement signal and the second measurement signal being evaluated independently of one another and whether a particle condition has been satisfied in the liquid region subsequently being determined on the basis of the evaluated first measurement signal and the evaluated second measurement signal.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

Provided are a raw material particle size distribution measuring apparatus and a particle size distribution measuring method. Also provided is a porosity measuring apparatus. The raw material particle size distribution measuring apparatus includes: a coarse particle measuring device that acquires information indicating the particle size distribution of the coarse particles; a fine particle measuring device that acquires information indicating the particle size distribution of the fine particles; and an arithmetic device that computes the particle size distribution of the coarse particles using the information indicating the particle size distribution of the coarse particles, computes the particle size distribution of the fine particles using the information indicating the particle size distribution of the fine particles, and computes an overall particle size distribution of the raw material using the particle size distribution of the coarse particles and the particle size distribution of the fine particles.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

Methods and systems are provided for use in evaluating quality of pollen grains. One example method includes germinating pollen grains of a crop in a fluid media and inserting a sample of the germinated pollen grains and fluid media into a flow chamber of a fluid imaging system. The sample flows through the flow chamber at a predefined rate, and images of the sample are captured by a camera of the fluid imaging system. The method then includes comparing the captured images of the sample to reference images specific to the crop and, based on the comparison, generating a pollen germination score for the sample, the pollen germination score indicative of germinated pollen grains in the sample.

An image analysis apparatus and an image analysis method capable of appropriately analyzing whether an analysis target cell is an abnormal cell even when a subject has a chromosomal abnormality are provided. An image analysis apparatus 10 includes light sources 121 and 122 configured to irradiate light onto a sample 21 whose target portion is labeled, an imaging unit 154 for capturing light generated from the sample 21 by irradiation with light, and a processing unit 11 that processes the image captured by the imaging unit 154. The processing unit 11 acquires information on the chromosomal abnormality and analyzes the image based on the information on the acquired chromosome abnormality.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

Methods and systems for displaying images of cells in a sample include obtaining a plurality of images of cells in the sample, where each image corresponds to one of the cells in the sample, determining values of at least one property for each of the cells based on the plurality of images, arranging the plurality of images to form a first image array, where the images are ordered in the first image array based on the values of the at least one property, displaying the first image array, sorting the plurality of images to form a second image array in which an ordering of the images is different from the first image array, and displaying the second image array, where the sample includes blood and the cells include red blood cells.