Abstract: Provided is an information processing device that includes a first information acquisition section that acquires first information on the basis of a still image in a frame corresponding to a predetermined time point, from among a plurality of images of a biological sample captured in a time-series manner, a second information acquisition section that acquires second information on the basis of an interframe change of the plurality of images in a predetermined period, and a determination section that determines an event regarding the biological sample, using the first information and the second information.

Abstract: In an image acquisition device, when capturing an optical image of a sample S through lane scanning, the number of tile images T included in a tile image row R acquired in one lane is counted, and a determination is made as to whether or not the number of images reaches a planned acquisition count that is set in advance. In a case where it is determined that the number of images does not reach the planned acquisition count, lane scanning with respect to the lane is re-executed. Accordingly, even when a loss of the tile images T occurs due to an environment load, the tile images T are complemented by re-execution of the lane scanning, and thus it is possible to prevent the loss of the tile images T in an observation image.

Abstract: Various image diagnostic systems, and methods of operating thereof, are disclosed herein. Example embodiments relate to operating the image diagnostic system to identify one or more tissue types within an image patch according to a hierarchical histological taxonomy, identifying an image patch associated with normal tissue, generating a pixel-level segmented image patch for an image patch, generating an encoded image patch for an image patch of at least one tissue, searching for one or more histopathological images, and assigning an image patch to one or more pathological cases.

Abstract: Provided are: a past relation identification unit that combines past sensor data with job data based on information on an item relating to data combination, and identifies a data relation between items based on an item of the past sensor data and an item of the job data; a real-time relation identification unit that identifies a data relation between items of real-time sensor data; a similarity calculation unit that calculates a similarity between the data relation identified by the past relation identification unit and the data relation identified by the real-time relation identification unit; and an output controller that outputs the data relation identified by the past relation identification unit and the data relation identified by the real-time relation identification unit while associating with each other by the item of the past sensor data and the item of the real-time sensor data, the similarity between those data relations exceeding a threshold, the similarity being calculated by the similarity calcu

Abstract: A method for detecting lens cleanliness of a lens in a flat-field optical path, the flat-field optical path includes a light source, the lens, a camera, the light source is a narrow-band multispectral uniform surface light source, the camera's light-sensitive surface is disposed perpendicular to an optical axis of the lens and in the light position of the lens and a pupil interposed between the lens and the light source, the method including collecting the bright-field image data and dark-field image data for a plurality of pupil aperture sizes through the lens; for each pixel, performing a pupil differential flat field correction to yield a plurality of PiPj pupil differentials; and displaying the pupil differentials in the form of a plurality of images to show uniformity of each image, wherein a non-uniform area on each image is determined to have been caused by an impurity of the lens.

Abstract: In a system and method for determining vital sign information of a subject the subject is illuminated with radiation, and radiation reflected from the subject is received. A region of interest is located in a first phase. Said illumination is controlled to locally illuminate, in a second phase, the located region of interest with radiation allowing determination of vital sign information. Finally, vital sign information of the subject is determined from the radiation reflected from said region of interest and detected in said second phase.

Abstract: Methods and devices for cytometric analysis are provided. A cytometry apparatus is provided which may be used with a stationary sample cuvette for analysis of a stationary sample or with a flow sample cuvette for analysis of a flowing sample. The methods and devices provided herein may be used to perform cytometric analysis of samples under a wide range of experimental and environmental conditions.

Abstract: A method for machine-learning based process flow recommendation is provided. The method may include training a machine-learning model by at least processing training data with the machine-learning model. The training data may include a matrix representing one or more existing process flows by at least indicating actions that are performed on a document object to generate a subsequent document object. An indication that a first document object is created as part of a process flow may be received. In response to the indication, the trained machine-learning model may be applied to generate a recommendation to perform, as part of the process flow, an action to generate a second document object. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include obtaining at least one MxIF image of a same tissue sample, obtaining information indicative of locations of cells in the at least one MxIF image, identifying multiple groups of cells in the at least one MxIF image at least in part by determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image and grouping the at least some of the cells into the multiple groups using the determined feature values, and determining at least one characteristic of the tissue sample using the multiple cell groups.

Abstract: A method is used for optical scanning of at least one object region placed on a transparent specimen holder. The method is as follows: for each sample lateral position of plural predefined sample lateral positions performing a focus determination by: performing laser reflection and using a first camera taking plural first images to determine a reference distance between the specimen holder and an objective lens; performing transmission flash illumination and using a second camera taking plural second images to define a focus distance taking into account the reference distance; after completing the focus determination, determining a focus distance topology across the object region based on the focus distances determined for ail sample lateral positions; and laterally moving the specimen holder and acquiring third images while focusing according to the focus distance topology.

Abstract: Systems and methods for capturing a digital image of a slide using an imaging line sensor and a focusing line sensor. In an embodiment, a beam-splitter is optically coupled to an objective lens and configured to receive one or more images of a portion of a sample through the objective lens. The beam-splitter simultaneously provides a first portion of the one or more images to the focusing sensor and a second portion of the one or more images to the imaging sensor. A processor controls the stage and/or objective lens such that each portion of the one or more images is received by the focusing sensor prior to it being received by the imaging sensor. In this manner, a focus of the objective lens can be controlled using data received from the focusing sensor prior to capturing an image of a portion of the sample using the imaging sensor.

Abstract: According to one embodiment, a medical information processing apparatus includes processing circuitry. The processing circuitry is configured to acquire information on an accuracy of a trained model that outputs information on a lesion included in data related to a medical image based on inputted data related to the medical image. Further, the processing circuitry is configured to determine a data collection condition for the medical image in a medical image diagnostic apparatus based on the information on the accuracy.

Abstract: Disclosed are a method for computer-assisted evaluation of biomarkers in a tissue sample as well as a corresponding system and computer program. The method includes the following steps: providing an image of the tissue sample in which cell nuclei of the tissue as well as biomarkers are represented, automatically determining for a plurality of locations or regions within the image a blood vessel probability that represents a probability that the image at the corresponding location or region represents a blood vessel, automatically determining an assumed blood vessel location within the image based on a distribution of blood vessel probability with respect to the locations or regions within the image, automatically selecting biomarkers represented in the image at locations outside the assumed blood vessel location but within a threshold distance from the assumed blood vessel location, and automatically evaluating the selected biomarkers.

Abstract: An image processing device and an image processing method capable of clarifying a specific region, especially a stained region of the cell nucleus, by correcting in consideration of overlap of staining, thereby making discrimination easy, and a pathological diagnosis assistance system using the same are provided. A pathological stained image is decomposed into dye concentration images of respective dyes, dye information on the respective staining dyes are correlated, and the respective dye concentration images are corrected to emphasize a specific region.

Abstract: As the capabilities of digital histopathology machines grows, there is an increasing need to ease the burden on pathology professionals of finding interesting structures in such images. Digital histopathology images can be at least several Gigabytes in size, and they may contain millions of cell structures of interest. Automated algorithms for finding structures in such images have been proposed, such as the Active Contour Model (ACM). The ACM algorithm can have difficulty detecting regions in images having variable colour or texture distributions. Such regions are often found in images containing cell nuclei, because nuclei do not always have a homogeneous appearance. The present application describes a technique to identify inhomogeneous structures, for example, cell nuclei, in digital histopathology information. It is proposed to search pre-computed super-pixel information using a morphological variable, such as a shape-compactness metric, to identify candidate objects.

Abstract: The present disclosure provides a microbe engineering platform that permits optimization of microbe fitness levels to optimize a microbe's suitability for industrial fermentation. The disclosed platform identifies an association between microbe properties and microbe fitness levels. The association between microbe properties and microbe fitness levels may be used to identify candidate microbes with desired fitness levels. The identified candidate microbes may be used to further optimize the industrial fermentation process.

Abstract: 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.

Abstract: An evaluation device includes a state determination unit which is configured to determine a state of cells which are an observation target under non-standard conditions on the basis of information acquired from an image of cells under standard conditions.

Abstract: An image processing apparatus includes an image acquirer for obtaining an original image of a pathological specimen, an information acquirer for obtaining finding information relating to pathological finding and an image generator for generating an output image including a processed image obtained by superimposing visual information based on the finding information on a source image. A relative density between the source image and the visual information is set according to an operation input from a user. Processing modes for generating the output image include a first mode for generating the output image in which the source image and the processed image are arranged on one screen and a second mode for generating the output image in which a wide range image at a relatively low magnification and an enlarged image representing a partial region in the wide range image at a higher magnification on one screen.

Abstract: Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.

Abstract: A computer-implemented method for locating possible artifacts in a virtually stained histology image, which was obtained from an initial histology image, is provided. In the method, at least one further image is generated on the basis of the virtually stained histology image or the initial histology image. The differences between the at least one further image and the virtually stained histology image are determined when the at least one further image has been generated on the basis of the initial histology image or the differences between the at least one further image and the initial histology image are determined when the at least one further image has been generated on the basis of the virtually stained histology image, and a mapping from the determined differences to their positions in the virtually stained histology image is created. The positions in the virtually stained histology image represent the positions of possible artifacts.

Abstract: Heterogeneity for biomarkers in a tissue sample can be calculated. A heterogeneity score can be combined with an immunohistochemistry combination score to provide breast cancer recurrence prognosis. Heterogeneity can be based on percent positivity determinations for a plurality of biomarkers according to how many cells in the sample stain positive. An immunohistochemistry combination score can be calculated. An imaging tool can support a digital pathologist workflow that includes designating fields of view in an image of the tissue sample. Based on the fields of view, a heterogeneity metric can be calculated and combined with an immunohistochemistry combination score to generate a breast cancer recurrence prognosis score.

Abstract: Systems and methods are provided to facilitate consultations between a referral source (e.g., labs, pathologists and patients) and a consultant (e.g., pathologist, radiologist, or other digital image analyst). Links between the various referral sources and consultants are established through a scanning center via a data communication network such as the Internet. The referral source sends a slide to the scanning center where the corresponding digital slide is posted for review and analysis by the consultant. Upon completion of the analysis and report, a digital slide conference is conducted through the scanning center that provides a venue for direct communication regarding the consultation. The scanning center may also facilitate payment from the referral source to the consultant.

Abstract: Method and apparatus for segmenting a cellular image are disclosed. A specific embodiment of the method includes: acquiring a cellular image; enhancing the cellular image using a generative adversarial network to obtain an enhanced cellular image; and segmenting the enhanced cellular image using a hierarchical fully convolutional network for image segmentation to obtain cytoplasm and zona pellucida areas in the cellular image.

Abstract: Method and system for grading a tumor. For example, a system for grading a tumor comprising: an image obtaining module configured to obtain a pathological image of a tissue to be examined; a snippet obtaining module configured to obtain one or more snippets having one or more sizes from the pathological image; an analyzing module configured to obtain one or more classification features based on at least analyzing the one or more snippets using one or more selected trained detection models of the analyzing module, wherein each selected trained detection model is configured to identify one or more classification features; and an outputting module configured to determine a tumor identification result based on at least the one or more classification features and output the tumor identification result.

Abstract: Large digital images are classified by analyzing them at a subimage level and assigning classification probabilities to the subimages; these may be combined into a classification probability for the entire image. Classification probabilities may be visualized across the image using probabilities computed for the subimages. This enables ready identification of the image subregions upon which classification is based as well as the classification scores or probabilities associated therewith. For example, a large source image—too large to be analyzed directly by a neural network—may be decomposed into smaller subimages such as square tiles, which are sifted based on a visual criterion. The visual criterion may be image entropy, density, background percentage, or other discriminator. A neural network produces tile-level classifications that are aggregated to classify the source image, and overlapping tiles are used to create a probability map showing subimage probabilities.

Abstract: An analysis system is configured to acquire data of a first numeric feature combination by referring to the parent population data. The data of the first numeric feature combination includes a score of a target indicator for each of category combinations. Each category combination of the category combinations is composed of categories of the numeric features of the first numeric feature combination. The analysis system is configured to determine a score at coordinates corresponding to each of the category combinations in a space having each of the numeric features of the first numeric feature combination as an axis, calculate a gradient feature vector representing a gradient for the score in the space, and determine, based on the gradient feature vector, whether to include the first numeric feature combination in numeric feature combinations to be extracted from the parent population data.

Abstract: Methods and apparatuses are disclosed herein to correct for inconsistencies in CT scans based on pi-lines. An example method at least includes acquiring a plurality of projections of a sample, each projection of the plurality of projections acquired at a different location around the sample based on a trajectory, determining pairs of opposing projections from the plurality of projections based on a respective pi-line, and determining an amount of inconsistency between respective pi-line data for each pair of opposing projections, where the pi-line data is based, at least in part, on attenuation data.

Abstract: Reaction values indicating based on which areas in an input image a plurality of identification results representing the states of cells or tissues are judged are calculated, and differences between a plurality of reaction values are obtained to thereby calculate the degree to which cells or tissues in an area of interest likely are in each state. Thereby, each state of cells or tissues can be visualized at an accurate position in an area of interest; therefore, diagnostic assistance to pathologist becomes possible.

Abstract: The present invention relates to methods to determine the identity of one or more organisms present in a sample (if these are already reported in a taxonomic database) or the identity of the closest related organism reported in a taxonomic database. The present invention does this by comparing a data set acquired by analyzing at least one component of the biological sample to a database, so as to match each component of the analyzed content of the sample to one or more taxon(s) and then collating the phylogenetic distance between each taxa and the taxon with the highest number of matches in the data set. A deconvolution function is then generated for the taxon with the highest number of matches, based on a correlation curve between the number of matches per taxon (Y axis) and the phylogenetic distance (X axis), the outcome of this function providing the identity of the organism or the closest known organism to it.

Abstract: A system and method of displaying of multiple simultaneous views of a same region of a biological tissue sample. Logical instructions are executed by a processor to perform operations such as receiving a plurality of images of the biological tissue sample, converting the plurality of images to a common reference frame based on the individual metadata of each image, and arranging the plurality of images into a display pattern for simultaneous viewing of different aspects of the imaged biological tissue sample on a display screen. The plurality of images is produced by preprocessing images of the biological tissue sample. Each image shows a view mode of a same region of the biological tissue sample, and each image contains metadata that describe spatial orientation, such as the translation, rotation, and magnification, of the image to bring the plurality of images to a common view.

Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.

Abstract: A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.

Abstract: An ophthalmic device that includes an image capturing unit configured to capture a tomographic image of a crystalline lens of a subject eye; a processor; and a memory storing computer-readable instructions therein. The computer-readable instructions, when executed by the processor, may cause the processor to execute: detecting a boundary between tissues in a tomographic image of the crystalline lens of the subject eye captured by the image capturing unit; determining whether the tissues defined by the boundary include an abnormality; and analyzing an analysis item associated with the abnormality.

Abstract: Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

Abstract: The present disclosure provides systems and methods for separating colors in an image by automatically selecting color reference vectors that take into consideration the effect of light scattering, and principally how the light scattering changes the proportions of RGB channel signals in detected light at varying stain concentrations.

Abstract: A method includes obtaining a set of image segment identigens for each image segment of an image to produce sets of image segment identigens. The sets of image segment identigens are possible interpretations of an image segment. The method further includes generating a set of relationships between image segments. The relationships provide a list of one or more ways in which the image segments are related. The method further includes processing different permutations of the sets of image segment identigens in accordance with the set of relationships to generate an entigen group. The entigen group represents a most likely interpretation of the image.

Abstract: A method of living body detection performed with a terminal device includes the following operations. A first image for a target object is obtained via a camera at a first focal length, and a second image for the target object is obtained via the camera at a second focal length. A difference image of the first image and the second image is determined. Whether the target object is a living body is determined according to the difference image.

Abstract: In one aspect, a computer implemented method for extracting information on actions or activities captured in digital data includes steps of: providing a first digital data set, extracting and characterizing first features from the first digital data set, creating first clusters from the extracted first features according to probability density functions and reference function distance calculations, providing a second digital data set, extracting and characterizing second features from the second digital data set, characterizing the extracted second features according to probability density functions and reference function distance calculations, and matching the second extracted features to a portion of the first extracted features clusters according to similarities in reference function distance calculations.

Abstract: In some aspects, the described systems and methods provide for a method for predicting tissue characteristics for a pathology image. A statistical model trained on multiple annotated pathology images is used. Each of the training pathology images includes an annotation describing tissue characteristics for one or more portions of the image. The method includes accessing a pathology image for predicting tissue characteristics. A trained statistical model is retrieved from a storage device. A set of patches is defined from the pathology image. Each of the patches in the set includes a subset of pixels from the corresponding pathology image. The set of patches is processed using the trained statistical model to predict respective annotations for each patch in the set. The predicted annotations are stored on the storage device.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: A depth estimating apparatus operated by at least one processor includes: a database which stores a photographed first color image, a training thermal image geometrically aligned with the first color image, and a second color image simultaneously photographed with the first color image as a training image set; and a training apparatus which trains a neural network in an unsupervised manner to output a chromaticity image and a binocular disparity image from the training thermal image. The training apparatus generates an estimated first color image from the second color image, the chromaticity image, and the binocular disparity image, and trains the neural network to minimize a difference between the estimated first color image and the photographed first color image.

Abstract: A microscope device connected to a network includes a communication unit and a control unit. The communication unit receives master setting information from a data processor by communicating with the data processor over the network, and the master setting information includes multiple pieces of customizing setting information that correspond to multiple types of microscopy. The control unit registers or updates own-device-setting information of the microscope device in accordance with the master setting information received by the communication unit.

Abstract: In view of such problems, the present invention provides a sample processing apparatus, a sample processing system, and a method of calculating measurement time, which allow an operator to efficiently capture images of cells. This invention relates to a sample processing apparatus 10 for measuring and analyzing a measurement sample 22. The sample processing apparatus 10 includes a light source 121 that irradiates light to the measurement sample 22, an imaging unit 154 that images light generated from the measurement sample 22 by irradiation with light, concentration information of cells included in the measurement sample 22 And a processing unit 11 that calculates a time required for cell imaging. The required time includes the time required for the processing of the image captured by the imaging unit 154 and the time required for the analysis of the image processed by the imaging unit 154.

Abstract: Approaches for classifying training samples with minimal error in a neural network using a low complexity neural network classifier, are described. In one example, for the neural network, an upper bound on the Vapnik-Chervonenkis (VC) dimension is determined. Thereafter, an empirical error function corresponding to the neural network is determined. A modified error function based on the upper bound on the VC dimension and the empirical error function is generated, and used for training the neural network.

Abstract: An observation apparatus includes: an area calculation unit that calculates a colony area based on an image in which a cell colony is captured; a cell number calculation unit that calculates, based on the image, the number of cells included in a target colony of which an area is calculated by the area calculation unit; and a density calculation unit that calculates, based on the area of the target colony calculated by the area calculation unit and the number of the cells included in the target colony calculated by the cell number calculation unit, a density of the cells included in the target colony.

Abstract: A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains data on medical actions and data on differences between planned medical actions or achievement objectives of treatment and results thereof. The processing circuitry extracts relevant factors associated with the differences based on the data on the medical actions and the data on the differences. The processing circuitry the relevant factors by allocating elements included in classification criteria to the relevant factors. The processing circuitry displays the relevant factors on a display for each of the classification criteria.

Abstract: An automated tissue section slicing, staining, and imaging system efficiently registers full-resolution tissue section images by applying scaled transformation matrices computed to register downsampled tissue section images to the full-resolution tissue section images.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: The present application relates to a method for the cytometric analysis of multiple cell samples by a microscope for examining multiple cell samples under a microscope, wherein the microscope can be or is operated, selectively and/or alternatingly, in a transmission mode and/or in a fluorescence mode, and wherein at least one cell sample has at least one fluorescence marker. The method includes; moving the cell samples continuously in one plane relative to an optical system of the microscope having at least one microscope camera, wherein, during the movement of the cell samples, at least one or more images of a sub-region of the cell samples are recorded in the transmission mode or in the fluorescence mode and at least one or more images of the same sub-region of the cell samples are recorded in the fluorescence mode by the at least one microscope camera.