Abstract: Disclosed is a computer-implemented method of determining an assignment of an object acquire patient image data of interest recognizable in a digital medical patient image such as a tumour or other medical anomaly such as an implant to an anatomical region. The medical patient image is registered with atlas data, The assignment is then determined by calculating a score value defining an amount of volume intersection between the object of interest and a digital object defining a specific anatomic region, for example a bounding box around a specific organ, which is defined in the atlas data.

Abstract: A method for determining part wear, such as using a wear metric, includes receiving, from a sensor, sensor data representing a surface of a wear part. The method further includes determining distances between measured points in the sensor data and points on one or more part models, which part models may include new part models and/or worn or wear limit part models. The method further includes using a bounding model that at least partially envelopes the part model(s) and the measured points to determine a direction along which the distances are measured. The method may also include quantifying wear using the measured distances.

Abstract: Disclosed are systems, devices and methods for imaging and image-based sorting of particles in a flow system, including cells in a flow cytometer. In some aspects, a system includes a particle flow device to flow particles through a channel, an imaging system to obtain image data of a particle during flow through the channel, a processing unit to determine a property associated with the particle and to produce a control command for sorting the particle based on sorting criteria associated with particle properties, and an actuator to direct the particle into one of a plurality of output paths of the particle flow device in real-time.

Abstract: An apparatus and method for lane feature detection from an image is performed according to predetermined path geometry. An image including at least one path is received. The image may be an aerial image. Map data, corresponding to the at least one path and defining the predetermined path geometry is selected. The image is modified according to the selected map data including the predetermined path geometry. A lane feature prediction model is generated or configured based on the modified image. A subsequent image is provided to the lane feature prediction model for a prediction of at least one lane feature.

Abstract: Systems and method for providing misaligned image features. A method includes receiving a first and a second image data set, wherein the first and the second image data sets map at least partially a shared examination region of an examination object, registering the first image data set with the second image data set, determining a distance data set based on the registered first image data set and the second image data set, identifying the misaligned image features in the distance data set that are caused by a misalignment between the registered first and the second image data sets, and providing the identified misaligned image features.

Abstract: Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

Abstract: A method for quantitatively measuring internal defects in an as-cast steel product includes optically scanning at least a portion of a surface of the steel product with a scanning device to create a digital image thereof, analyzing the digital image to calculate a quantitative value for an amount of internal defects therein, and normalizing the quantitative value to a rating according to a standardized scale.

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 is an observation device including: a stereo image-acquisition optical system that acquires images of cells floating in a culture fluid inside a culture vessel; and an analyzer that calculates a cell density of the cells on the basis of the images acquired by the stereo image-acquisition optical system, wherein the analyzer identifies a three-dimensional position of each of the cells included in the images and calculates the cell density on the basis of the number of cells present within a predetermined three-dimensional region.

Abstract: Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Abstract: Disclosed are a cranial CT-based grading method and a corresponding system, which relate to the field of medical imaging. The cranial CT-based grading method as disclosed solves the problems of relatively great subjective disparities and poor operability in eye-balling ASPECTS assessment. The grading method includes: extracting target areas from to-be-processed multi-frame cranial CT data; performing infarct judgment on each target area included in the target areas to output an infarct judgment outcome regarding the target area; and outputting a grading outcome based on infarct judgment outcomes regarding all target areas. The grading method and system as disclosed may eliminate or mitigate the diagnosis disparities caused by human factors and imaging deviations due to different imaging equipment, and shorten the time taken by human observation, consideration, and bared-eye grading, thereby serving as a computer-aided method to provide reference for medical studies on stroke.

Abstract: Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and ?-methylacyl CoA racemase (AMACR).

Abstract: The present disclosure provides methods of preparing a biological specimen for imaging analysis, comprising fixing and clearing the biological specimen and subsequently analyzing the cleared biological specimen using microscopy. Also included are methods of quantifying cells, for example, active populations of cells in response to a stimulant. The present disclosure also provides devices for practicing the described methods. A flow-assisted clearing device provides rapid clearing of hydrogel-embedded biological specimens without the need of specialized equipment such as electrophoresis or perfusion devices.

Abstract: The present invention relates to a blood vessel image processing method, a blood vessel image processing apparatus, a computer storage medium, and an imaging device. The method includes: obtaining blood vessel geometric structure information of a blood vessel segment of interest; obtaining vital feature information of the blood vessel segment; establishing an association relationship between the blood vessel geometric structure information and the vital feature information; and displaying the blood vessel geometric structure information and the vital feature information in the same image in a mutual fusion manner by using the association relationship as a reference. In this way, work efficiency of users can be improved by the solution.

Abstract: An image processing method includes: calculating an area of a first label of a first medical image of a plurality of medical images with a plurality of labels; obtaining a first determination result based on whether the area of the first label is greater than a threshold value; obtaining a second determination result based on whether a second medical image of the medical images adjacent to the first medical image includes a second label overlapping a first projection area of the first label on the second medical image; and selectively deleting the first label on the first medical image according to the first determination result and the second determination result. The present disclosure further provides an image processing system to perform the image processing method.

Abstract: Some embodiments are directed to a process for quantifying changes in the intestinal mucosa caused by a chronic inflammatory intestinal disease in individuals, an ex vivo process for diagnosing a chronic inflammatory intestinal disease in individuals, and an ex vivo process for the differential diagnosis of Crohn's disease versus ulcerative colitis in individuals.

Abstract: Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Abstract: A new chest X-ray database, referred to as “ChestX-ray8”, is disclosed herein, which comprises over 100,000 frontal view X-ray images of over 32,000 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. We demonstrate that these commonly occurring thoracic diseases can be detected and spatially-located via a unified weakly supervised multi-label image classification and disease localization framework, which is validated using our disclosed dataset.

Abstract: A system for facilitating medical image interpretation includes a processing unit and a display control unit. The processing unit includes a location information module generating a reference location indicator, and a feature marking module generating indication markers. The display control unit is in signal connection with the processing unit and a display device. The display control unit includes an image displaying module controlling the display device to display tissue images, and an auxiliary information displaying module controlling the display device to display, for each of the tissue images displayed by the display device, the reference location indicator and the indication markers together on the tissue image.

Abstract: A mammography apparatus includes a diagnostic image acquisition unit that acquires a diagnostic image in which a calcification as a biopsy target is marked; a scout image acquisition unit that acquires a scout image obtained by imaging a mamma undergoing the biopsy from a specific direction; and a display unit that highlights a calcification (candidate for biological tissue examination) in the scout image which matches at least the marked calcification in the diagnostic image.