The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.

The invention relates to a localization microscopy method for localizing signal sources. Here, at least once for each pixel of a detector, values of an error parameter are ascertained and stored in a calibration data record in a manner assigned to the relevant pixel. Captured image data are used to identify regions of origin of signal sources and fit a point spread function to the pixel values of the respective regions of origin. The respective signal source is localized on the basis of the point spread function. The pixel-specific error parameter of each pixel can be compared to a threshold. If the threshold is exceeded, these pixels are either ignored or replaced by means of interpolation when fitting the point spread function. In addition or as an alternative thereto, the real noise performance of the pixels is ascertained and corrected on the basis of derived pixel-specific error parameters.

Provided is an image-processing method including: an image-acquiring step of acquiring a divided-section image that includes the entire divided section by capturing an image of a chip array obtained by dividing a substrate into numerous chips together with a section of biological tissue on the substrate; a chip-recognizing step of recognizing chip images in the divided-section image; an attribute-information assigning step of assigning, to each of pixels that constitute the images of the recognized chips, positional information of the chip images to which those pixels belong in the image of the chip array; and a restoring step of generating a restored section image in which images of the divided section are joined into a single image by joining the chip images constituted of the pixels to which the positional information has been assigned.

Reciprocal space map of specific sample locations is generated based on the sample images acquired by irradiating the sample with a charged particle beam at multiple incident angles. The incident angles are obtained by tilting the charged particle beam and/or the sample around two perpendicular axes within the sample plane. The reciprocal space map of a selected sample location is generated based on intensity of pixels corresponding to the location in the sample images.

A system and method for analyzing multi-dimensional images includes a high content imaging system that includes an image capture device. An image acquisition module receives a series of images of a biological sample captured by the image capture device, and the series of images includes a sequence of image planes. A human interface module receives from a user computer specifications of a first image analysis step and a second image analysis step. The first image analysis step specifies a first image processing operation that processes an image plane of a series of images in accordance with at least another image plane of the series of images and the second image analysis step specifies a second image processing operation that processes each image plane of a series of images independently of the other image planes of the series. An image analysis module having a plurality of processors operating in parallel processes the first series of images in accordance with the first image processing step to generate a first output series of images, and processes the first series of images in accordance with second image processing step to generate a second output series of images. The human interface module displays at least one image plane of the first output series of images and at least one image plane of the second output series of images on a display associated with the user computer.

A sample imaging apparatus comprising: an imaging section for imaging a stained sample including a stained cell to generate a cell image relating to the stained cell included in the stained sample; and a staining abnormality detector for detecting an abnormality relating to staining of the stained sample on the basis of the cell image generated by the imaging section, is disclosed. A sample imaging apparatus comprising: an imaging section for imaging a stained sample to generate a cell image relating to a cell included in the stained sample; and an imaging abnormality detector for detecting an abnormality relating to the imaging section on the basis of the cell image generated by the imaging section, is also disclosed.

A method of generating a codebook includes obtaining a plurality of gene-identifying code words for the codebook. Each gene-identifying code word is represented by a sequence of N bits that correspond to a best match to a pixel data value identifying a gene. A plurality of negative control code words is generated, and each negative control code word is represented by a sequence of N bits. The negative control code words have an equal number of on-values. On-values of the plurality of negative control code words are evenly distributed across the N bits such that each ordinal position in the sequence of N bits has a same total number of on-bits from the plurality of negative control code words, and a Hamming distance between each negative control code word and each gene-identify code word is at least a distance threshold.

A system and method of biological testing and deep learning to predict fertility based on ferning patterns and detecting white blood cells in cervical mucous samples.

A mobile phone-based miniature microscopic image acquisition device, and image stitching and recognition methods are provided. The acquisition device comprises a support, wherein a mobile phone fixing table is provided on the support. A microscope head is provided below a camera of a mobile phone. A slide holder is provided below the microscope head, and an lighting source is provided below the slide holder. A scanning movement is performed between the slide holder and the microscope head along X and Y axes, so that images of a slide are acquired into the mobile phone. The slide sample images acquired into the mobile phone can be stitched and recognized, and can be uploaded to the cloud to be processed by cloud AI, thereby significantly improving the accuracy and efficiency of cell recognition, greatly reducing the medical cost, and ensuring more remote medical institutions can apply such technology for diagnosis.

Disclosed is a method for characterizing a sample by mass spectrometry imaging (MSI) according to which a spatial arrangement of at least one ion in the sample is characterized from imaging data associated with the ion, in terms of morphology and/or texture.