Abstract: An image classification method, apparatus, and device. The method includes: inputting a to-be-classified image dataset, where the image dataset includes to-be-classified images; cropping each of the to-be-classified images with a center of the to-be-classified image as a reference center point to obtain an image having a preset size and including a specific region as a standard image; selecting a pixel array of any single channel in the standard image and drawing a corresponding signal waveform graph based on the pixel array; and determining a type of the to-be-classified image. After the standard image including the specific region is obtained by cropping the to-be-classified image, the pixel array of the any single channel is selected from the standard image, and then the signal waveform graph is drawn based on the selected pixel array of the single channel, to classify the to-be-classified image based on the drawn signal waveform graph.

Abstract: A radiology workstation (24) includes at least one display component (30, 32); at least one user input device (28); and at least one microprocessor (26, 34) programmed to generate a contextual ranking of clinical codes for a context received via the at least one user input device (28) and to display information pertaining to the contextual ranking on the display component (30, 32) of the radiology workstation (24). The contextual ranking is computed by the microprocessor from (i) statistics of occurrences of the clinical codes in radiology reports contained in a radiology reports database (10) and satisfying the context and (ii) statistics of the clinical codes in problem lists contained in a problem lists database and satisfying the context.

Abstract: A prediction model is provided which is capable of predicting a correctness of a segmentation by a segmentation algorithm. The prediction model may be trained using a machine learning technique, and after training used to predict the correctness of a segmentation of a boundary in respective image portions of an image by the segmentation algorithm. The predicted correctness may then be visualized, for example as an overlay of the segmentation.

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

Abstract: Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.

Abstract: A medical auxiliary information generation method and a medical auxiliary information generation system are provided. The method includes: obtaining a physiological image; identifying a target image region from the physiological image through at least one artificial intelligence model, wherein each pixel position in the target image region corresponds to an estimation value, and the estimation value meets a default condition; determining at least one target pixel position in the target image region according to a numerical distribution of the estimation value; and generating medical auxiliary information according to the target pixel position, wherein the medical auxiliary information carries prompt information related to the target pixel position.

Abstract: Subjects who are potentially impacted by a medical condition are identified. An experimental group includes subjects having a positive indication for a specific criterion related to the medical condition in their medical profiles. A control groups includes subjects having a negative indication for the specific criterion. An artificial intelligence system is trained using the specific criterion and secondary characteristics of the subjects of the experimental and control groups to construct a classifier for the medical condition. The classifier is used to extract a target group of subjects from a population of subjects. A medical profile of each subject of the target group is marked as potentially affected by the medical condition. A system includes the artificial intelligence system and a database for storing the medical profiles. Deep learning or machine learning may be used to analyze medical images such as retinal images.

Abstract: Systems, methods, and apparatus are disclosed for determining quantitative hormone and chemical analyte results from qualitative test results. An image is taken of a test device. The image is analyzed to identify a darkness intensity ratio (T/C ratio) between a darkness value of a test-line to a darkness value of a control-line. Additionally, a quantitative substance level may be determined using the T/C ratio, by identifying the type of test device and referencing a data structure that relates quantitative substance levels to T/C ratios for the identified type of test device.

Abstract: A blood flow field estimation apparatus is provided, including an estimation unit that uses a learned model obtained in advance by performing machine learning to learn a relationship between organ tissue three-dimensional structure data including image data of a plurality of organ cross-sectional images serving as cross-sectional images of an organ and having each pixel provided with two or more bit depths and image position information serving as information indicating a position of an image reflected on each of the organ cross-sectional images in the organ, and a blood flow field in the organ, and estimates the blood flow field in the organ of an estimation target, based on the organ tissue three-dimensional structure data of the organ of the estimation target, and an output unit that outputs an estimation result of the estimation unit.

Abstract: Methods and systems for processing a scanned tissue section include locating cells within a scanned tissue. Cells in the scanned tissue are classified using a classifier model. A tumor-cell ratio (TCR) map is generated based on classified normal cells and tumor cells. A TCR isoline is generated for a target TCR value using the TCR map, marking areas of the tissue section where a TCR is at or above the target TCR value. Dissection is performed on the tissue sample to isolate an area identified by the isoline.

Abstract: An information processing apparatus includes at least one processor, and the processor derives a property for at least one predetermined property item which is related to a structure of interest included in an image. The processor specifies a basis region serving as a basis for deriving the property related to the structure of interest for each property item and derives a basis image including the basis region.

Abstract: A system for characterizing a region of interest of a biological tissue that includes a sighting device that produces an image in reflection of an elementary surface of the biological tissue. The system further includes a microscopic analysis device that detects, in a detection pattern included in the elementary surface, a light beam emitted by the biological tissue in response to illumination of the biological tissue and generates microscopic analysis information, and to determine a tissue characterization parameter from the microscopic analysis information. The system further includes a processing module that localizes, relative to a surface image of the region of interest, each elementary surface image of various elementary surface images and produces a map element for the characterization parameter. The system further includes a display module that displays a tissue map.

Abstract: Aspects of the present invention relate to an apparatus for determining alignment between a source device and a target device. The apparatus comprises the source device, the target device, and a processing system. The source device comprises a first spatially variant field generator for generating a first spatially variant electric field, and a second spatially variant field generator for generating a second spatially variant electric field, wherein the second spatially variant electric field is angularly offset with respect to the first spatially variant electric field. The target device comprises at least one sensor configured to detect a first signal from the first spatially variant electric field and a second signal from the second spatially variant electric field. The processing system is configured to determine alignment between the first source device and the target device based on the first and second signals from the first and second spatially variant electric fields by the target device.

Abstract: Provided herein are systems, methods, and media capable of determining estimated force applied on a target tissue region to enable tactile feedback during interaction with said target tissue region.

Abstract: Provided herein are augmented reality visual aid systems, software, and methods which enhance vision, to simulate natural vision, by utilizing hybrid see through occlusion enabled hardware, and software through image manipulation, reprocessing, blending, for presentation and display to the eyes thus enabling a range of tasks previously lost or impacted.

Abstract: An intraoral scanner system includes an intraoral scanner having an imaging device and a sensing face, and a computing device, communicatively coupled to the intraoral scanner. The computing device receives a first intraoral images of a three-dimensional intraoral object of a patient generated by the intraoral scanner corresponding to an intraoral scanning of the three-dimensional intraoral object of the patient. The computing device registers a first intraoral image of the first intraoral images relative to a second intraoral image of the first intraoral images using a model of the three-dimensional intraoral object that existed prior to the intraoral scanning.

Abstract: A method for generating a color-faithful extended-depth-of-field (EDF) image from a color volume of 2D images acquired at different focal depths using a microscope.

Abstract: A medical image processing device acquires a medical image, detects a position of a specific blood vessel having a predetermined thickness or more on the basis of the medical image, and performs control for outputting specific blood vessel position information regarding the position of the specific blood vessel to provide a notification. The detection of the position of a specific blood vessel is performed by using a learning image that is the medical image associated with information regarding a position of at least a part of the specific blood vessel.

Abstract: A system and method and system for providing preoperative patient intraoral scanning for diagnosis and preparation of a treatment plan utilizing the proposed system in advance of the crown surgery and intraoperative patient intraoral scanning for system assessment and verification for the method of treatment. More specifically, the proposed invention proposes a method and system of patient intraoral scanning to identify the best predesigned crown for use for the patient and develop a plan for intraoral surface treatment for the identified predesigned crown and automated crown inventory management.

Abstract: An inspection analysis method includes an analysis step, a prediction step, and an output step. A processor analyzes inspection information related to an operation in an inspection and generates first operation data and second operation data indicating a content of the operation in the analysis step. The first operation data indicate a content of the operation in a first inspection. The second operation data indicate a content of the operation in a second inspection performed after the first inspection is completed. The processor compares the first operation data and the second operation data with each other and predicts a state of an inspection target in the prediction step. The processor outputs state information indicating the state to a reporting device in the output step.

Abstract: A text generation unit (14) generates a plurality of texts which describe properties of feature portions and are different from each other for at least one feature portion included in an image. A display control unit (15) performs control such that each of the plurality of texts is displayed on a display unit.

Abstract: Disclosed is a method for analyzing a lesion based on a medical image, which is performed by a computing device. The method may include: obtaining positional information of a suspicious nodule which exists in the medical image; generating a mask for the suspicious nodule based on a patch of the medical image corresponding to the positional information; and determining a class for a state of the suspicious nodule based on the patch of the medical image and the mask for the suspicious nodule.

Abstract: An ophthalmic image of an evaluation target is acquired, a plurality of subsection images is extracted from the ophthalmic image, a state of a subject's eye is predicted for each of the subsection images based on a learned model in which learning has been performed in advance regarding extracting a plurality of subsection images from an ophthalmic image for learning, and predicting a state of a subject's eye for the each of subsection image by machine learning using correct answer data related to a state of each subsection image, and the subsection image is extracted from the ophthalmic image so as to have an image size corresponding to a state of a subject's eye of an evaluation target.

Abstract: A computer-implemented method for transforming magnetic resonance (MR) imaging across multiple vendors is provided. The method comprises: obtaining a training dataset, wherein the training dataset comprises a paired dataset and an un-paired dataset, and wherein the training dataset comprises image data acquired using two or more MR imaging devices; training a deep network model using the training dataset; obtaining an input MR image; and transforming the input MR image to a target image style using the deep network model.

Abstract: A method for determining a centerline of a blood vessel in an image associated with a subject is provided. The method includes obtaining a centerline model used for identifying a centerline of a blood vessel and identifying the centerline of the blood vessel based on the centerline model.

Abstract: Described here are systems and methods for separating magnetic resonance signals that are changing over a scan duration (i.e., dynamic signals) from magnetic resonance signals that are static over the same duration. As such, the systems and methods described in the present disclosure can be used to remove artifacts associated with dynamic signals from images of static structures, or to better image the dynamic signal (e.g., pulsatile blood flow or respiratory motion).

Abstract: A method for generating treatment recommendations based on images of a facial region of a human subject comprises the steps of acquiring at least one image of the facial region and generating image data representing the at least one image; processing the image data and additional information, including at least an indication of a condition affecting an appearance of the facial region and a treatment type to treat the condition, to obtain a treatment recommendation and outputting the obtained treatment recommendation. The processing step includes a comparison substep to compare information derived from the image data with reference information to obtain a reference measure. The processing step further includes a classifying substep to obtain the treatment recommendation (63.1 . . . 8, 64.1 . . . 8) based on the reference measure.

Abstract: Anatomical parts including surgical trainers and prosthesis and methods of creating three-dimensional anatomical parts by gathering three-dimensional image data from multiple sources, indexing characteristics from the data and then averaging the data to create new anatomical parts that have averaged characteristics. The disclosed methods also enable bonding of various materials through printed lattices.

Abstract: A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of work charts.

Abstract: A computer implemented method for reading a test region of an assay, the method comprising: (i) providing digital image data of a first assay; (ii) inputting the digital image data into a trained convolutional neural network configured to output a first probability, based on the input digital image data, that a first region of pixels of the digital image data corresponds to a first test region of the assay; (iii) if the first probability is at or above a first predetermined threshold, accepting the first region of pixels as a first region of interest associated with the first test region; and (iv) estimating an intensity value of a portion of the first test region in the first region of interest.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform a downstream computer vision task. One of the methods includes pre-training an initial neural network that shares layers with the neural network to perform an initial computer vision task and then training the neural network on the downstream computer vision task.

Abstract: This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.

Abstract: The present development is a method for providing stable visualization of tubular objects. The method simplifies visualization using a Fly-Ln approach without loss of internal surface details. The method uses graphical programming, and can be easily integrated with commercially available visualization programs, such as Virtual Colonoscopy (VC) or Computed Tomography Colonography (CTC).

Abstract: A system for visualizing a surgical site is provided. The system includes a robotic mechanism for performing a procedure on a patient, an imaging device coupled to the robotic mechanism, the imaging device configured to provide image data of a site of interest, and a computing device coupled to the imaging device. The computing device includes one or more processors and at least one memory device configured to store executable instructions. The executable instructions, when executed by the processor, are configured to receive the image data of the site of interest, track motion patterns of the site of interest in the received image data, filter the received image data to remove line-of-sight restrictions therein and alter pixels therein based on the tracked motion patterns, and generate an output frame from the filtered image data. The system also includes a presentation interface device coupled to the computing device and configured to present the output frame for visualization of the site of interest.

Abstract: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

Abstract: A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

Abstract: A care system and an automatic care method are provided. The care system is suitable for a care recipient. A physiological condition of the care recipient can be determined by sensors arranged around the care recipient, and a health condition of the care recipient can be determined through monitoring sleep disorders thereof. The care system receives a sound of the care recipient through a sound receiver, after removing a background sound, a sound of the care recipient can be obtained, and an image sensor is used to obtain an image of the care recipient to perform a motion detection, so as to obtain a posture image of the care recipient. Artificial intelligence technology can be used to build a care-taking warning prediction model to determine whether or not the status of the care recipient has reached a warning threshold.

Abstract: The invention refers to an apparatus for monitoring a subject (121) during an imaging procedure, e.g. CT-imaging. The apparatus (110) comprises a monitoring image providing unit (111) providing a first monitoring image and a second monitoring image acquired at different support positions, a monitoring position providing unit (112) providing a first monitoring position of a region of interest in the first monitoring image, a support position providing unit (113) providing support position data of the support positions, a position map providing unit (114) providing a position map mapping calibration support positions to calibration monitoring positions, and a region of interest position determination unit (115) determining a position of the region of interest in the second monitoring image based on the first monitoring position, the support position data, and the position map. This allows to determine the position of the region of interest accurately and with low computational effort.

Abstract: A method for generating a module configured to determine concentration of an analyte in a sample of a body fluid is disclosed. The method includes providing a first set of measurement data derived from images of one or more test strips indicating a color transformation in response to a body fluid containing an analyte. The images can be recorded by multiple devices with differing cameras, software and/or hardware device configurations for image recording and image data processing. A neural network model can be generated in a machine learning process applying an artificial neural network and a module configured to determine concentration of an analyte in a second sample of a body fluid can be generated. Further, the present disclosure includes a system for generating the module as well as a method and a system for determining concentration of an analyte in a sample of a bodily fluid.

Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

Abstract: A system is provided that includes a Q3D endoscope disposed to image a field of view and a processor that produces a Q3D model of a scene and identifies target instruments and structures. The processor is configured to display the scene from a virtual field of view of an instrument, to determine a no fly zone around targets, to determine a predicted path for said instruments or to provide 3D tracking of said instruments.

Abstract: A method for modeling a joint before, during, and/or after a medical procedure includes receiving first imaging data capturing the joint from a first imaging perspective and second imaging data capturing the joint from a second imaging perspective that is different than the first imaging perspective, the first and second imaging data generated intraoperatively via a two-dimensional imaging modality, generating three-dimensional image data by back-projecting the first and second imaging data in three-dimensional space in accordance with a relative difference between the first and second imaging perspectives, generating a three-dimensional model of the joint based on processing the three-dimensional image data with a machine learning model trained on imaging data generated via at least a three-dimensional imaging modality, and displaying a visualization based on the three-dimensional model of the joint during the medical procedure.

Abstract: A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining, by one or more processors, a first medical image, wherein the first medical image includes a representation of a target organ, and processing, by the one or more processors, the first medical image based at least in part on a first machine learning model. A classification result associated with the first medical image and a first image of a target region in the first medical image are obtained based at least in part on the processing of the first medical image using the first machine learning model.

Abstract: Systems and methods provide automated systems for accurately estimating infusion coverage within a target brain region in real-time (or approximately real-time) during surgical procedures. Such accurate and real-time infusion coverage estimation enables intraoperative monitoring and adjustment of infusion parameters (e.g., cannula tip location, infusate delivery flow rate, etc.) for achieving optimal/improved infusion coverage for a given drug therapy. Accordingly, examples of the presently disclosed technology can improve the efficacy and safety of drug therapies delivered to the brain.

Abstract: In a case where a first specific scene recognized at a time of insertion of an endoscope is stored in a specific scene memory and a recognized scene recognized by a scene recognition unit is a scene on a side deeper than the first specific scene in a direction of movement of the endoscope, the recognized scene is output without being changed. In a case where the first specific scene is stored in the specific scene memory and the recognized scene is a scene on a side shallower than the first specific scene in the direction of movement of the endoscope, the recognized scene is changed and output.

Abstract: Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

Abstract: Described herein are Deep Multi-Magnification Networks (DMMNs). The multi-class tissue segmentation architecture processes a set of patches from multiple magnifications to make more accurate predictions. For the supervised training, partial annotations may be used to reduce the burden of annotators. The segmentation architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other segmentation architectures on breast datasets, and can be used to facilitate pathologists' assessments of breast cancer in margin specimens.

Abstract: Embodiments include accessing a set of digital pathology (DP) images having an imaging parameter; applying a low-computational cost histology quality control (HistoQC) pipeline to the DP images, where the low-computational cost HistoQC pipeline computes a first set of image metrics associated with a DP image, and assigns the DP image to a first or a second, different cohort based on the imaging parameter and the first set of image metrics; applying a first, higher-computational-cost HistoQC pipeline to a member of the first cohort; applying a second, different higher-computation-cost HistoQC pipeline to a member of the second cohort; where the first or second, higher-computational-cost HistoQC pipeline determines an artifact-free region of the member of the first or second cohort, respectively, and classifies the member of the first or second cohort, respectively, as suitable or unsuitable for downstream computation or diagnostic analysis based, at least in part, on the artifact free region.