Disclosed herein are apparatuses, systems, methods, and computer-readable media relating to area selection in charged particle microscope (CPM) imaging. For example, in some embodiments, a CPM support apparatus may include: first logic to generate a first data set associated with an area of a specimen by processing data from a first imaging round of the area by a CPM; second logic to generate predicted parameters of the area; and third logic to determine whether a second imaging round of the area is to be performed by the CPM based on the predicted parameters of the area; wherein the first logic is to, in response to a determination by the third logic that a second imaging round of the area is to be performed, generate a second data set, including measured parameters, associated with the area by processing data from a second imaging round of the area by the CPM.

A method of automated measurement of motility and morphology parameters of the same single motile sperm. Automated motility and morphology measurements of the same single sperm are performed under different microscope magnifications. The same single motile sperm is automatically positioned and kept inside microscope field of view and in focus after magnification switch. A method of automated non-invasive measurement of sperm morphology parameters under high magnification of imaging. Sperm morphology parameters including subcellular structures are automatically measured without invasive sample staining. A method of automatically selecting sperms with normal motility and morphology and DNA integrity for infertility treatment.

A method for classifying images of oligolayer exfoliation attempts. In some embodiments, the method includes forming a micrograph of a surface, and classifying the micrograph into one of a plurality of categories. The categories may include a first category, consisting of micrographs including at least one oligolayer flake, and a second category, consisting of micrographs including no oligolayer flakes, the classifying comprising classifying the micrograph with a neural network.

A convolutional neural network (CNN) and associated method for identifying basal cell carcinoma are disclosed. The CNN comprises two convolution layers, two pooling layers and at least one fully-connected layer. The first convolution layer uses initial Gabor filters that model the kernel parameters setting in advance based on human professional knowledge. The method uses collagen fiber images for training images and converts doctors' knowledge to initiate the Gabor filters as featuring computerization. The invention provides better training performance in terms of training time consumption and training material overhead.

The present invention is in the field of experimental data acquisition. In particular, the present invention relates to a live-cell imaging method and a corresponding system for acquiring experimental data of one or more biological probes. More specifically, the present invention relates to methods and systems for evaluating an optimized concentration trajectory for administration of a drug, in particular a chemotherapeutic drug.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

A method of enhancing a resolution of an EDS image of a sample includes generating an EDS image of the sample, generating a non-EDS image of the sample generating, using a machine learning algorithm, an enhanced resolution EDS image of the sample based on the generated feature map and based on the first EDS image, where a resolution of the enhanced resolution EDS image is higher than a resolution of the first EDS image.

A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.