Abstract: A medical image processing device includes a storage and a processor. The storage stores notification setting information for setting a notification content regarding which method a notification is performed in accordance with lesion information indicating content of a lesion, part information representing a part, or procedure information representing a procedure performed at a time of acquisition of a medical image. The processor is configured to acquire the medical image including a subject; perform image analysis processing on the medical image; select use-notification setting information from the notification setting information by using the lesion information, the part information or the procedure information; and perform the notification to notify a user of a result of the image analysis processing with different methods of the notifications for different lesion information, part information or procedure information according to the use-notification setting information.

Abstract: When swinging of a camera does not follow a motion of a subject in following-photographing, the position of the subject deviates among a plurality of images, and thus there is a problem that the subject that is moving is blurred. An image processing apparatus includes: a standard region designation unit configured to designate a partial region in an image as a standard region; a standard region motion blur setting unit configured to set a motion blur in the standard region designated by the standard region designation unit as a predetermined motion blur; and a motion blur adjustment unit configured to adjust a motion blur in the standard region so that the motion blur becomes the predetermined motion blur and adjust a motion blur on a screen of a region different from the standard region in accordance with the adjusting of the motion blur in the standard region.

Abstract: Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.

Abstract: Image-based key points detection using a convolutional neural network (CNN) may be impacted if the key points are occluded in the image. Images obtained from additional imaging modalities such as depth and/or thermal images may be used in conjunction with RGB images to reduce or minimize the impact of the occlusion. The additional images may be used to determine adjustment values that are then applied to the weights of the CNN so that the convolution operations may be performed in a modality aware manner to increase the robustness, accuracy, and efficiency of key point detection.

Abstract: A tissue analysis system may include an ultrasound device to stimulate tissue with ultrasound waves causing the tissue to vibrate and produce a vibration signature, a vibration detector configured to detect the vibration signature, and a signal analysis processor. The signal analysis processor may be configured according to computer-executable instructions for accessing a signature library storing a plurality of vibration signatures each linked to one of a plurality of known tissues having a known identity, comparing the vibration signature of the tissue with the stored vibration signatures of the signature library, determining if a match between the vibration signature of the tissue and a stored vibration signature of the signature library is present and, if the match is present, determining an identity of the tissue based on the known identity of the known tissue having the matching stored vibration signature.

Abstract: A medical image processing device includes: a first captured image acquisition unit configured to acquire a first left-eye image and a first right-eye image; a second captured image acquisition unit configured to acquire a second left-eye image and a second right-eye image; a superimposed image generation unit configured to superimpose corresponding pixels of the first left-eye image and the second left-eye image on each other to generate a left-eye fluorescence superimposed image and superimpose corresponding pixels of the first right-eye image and the second right-eye image on each other to generate a right-eye fluorescence superimposed image; and a display controller configured to generate a display image from the first left-eye and right-eye images, the second left-eye and right-eye images, the left-eye and the right-eye fluorescence superimposed images, wherein the display image includes a stereoscopically observable three-dimensional fluorescence superimposed image generated from the left-eye and the ri

Abstract: In an object detection device for detecting an object, a single sensor unit includes a light emitting unit and a light receiving unit. The light receiving unit includes a two-dimensional array of light receiving elements and outputs a light reception signal in response to a light reception state of a set of the light receiving elements for each pixel. The sensor unit is configured to, based on the light reception signals, acquire first information indicating reception intensity of the reflected light at each pixel, second information indicating reception intensity of background light that is light other than the reflected light, at each pixel, and third information indicating a distance to the object at each pixel as a pixel value of the pixel. A detection unit is configured to use all of the first to third information included in the pixel values of the respective pixels to detect the object.

Abstract: A medical information processing system according to an embodiment includes processing circuitry and a display. On the basis of one of the type of at least one abnormality detection algorithm to which a medical image related to an examined subject is to be input and information relevant to an abnormality detected by inputting the medical image to the abnormality detection algorithm, the processing circuitry judges whether or not urgency is present in a disorder related to the abnormality. When it is determined that the urgency is present, the display displays, in an examination list of examination orders, assessment information related to assessing the abnormality and urgency information indicating the urgency so as to be positioned adjacent to any of the examination orders related to the abnormality.

Abstract: Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data augmentation. The method includes: generating a group of candidate images based on a target image by using a thermodynamic genetic algorithm (TDGA) model, the TDGA model being configured to apply one or more operations of a set of predetermined image processing operations during each evolution process; and determining multiple augmented images from the group of candidate images based on free energy of the group of candidate images, the multiple augmented images being determined as belonging to the same classification with the target image. In this way, data augmentation can be efficiently implemented by a thermodynamic genetic algorithm.

Abstract: A method for obtaining and processing image data by using a medical visual aid system including a monitor and an endoscope configured to be inserted into a body cavity and including an image capturing device and a light emitting device, the method including illuminating a field of view of the image capturing device with the light emitting device, capturing the image data using the image capturing device, providing a non-linear scaling model adapted to the body cavity, adjusting the image data by applying the non-linear scaling model such that adjusted image data is formed, and presenting the adjusted image data on the monitor.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.

Abstract: A method of reading a medical image by a computing device operated by at least one processor is provided. The method includes obtaining an abnormality score of the input image using an abnormality prediction model, filtering the input image so as not to be subsequently analyzed when the abnormality score is less than or equal to a cut-off score based on the cut-off score which makes a specific reading sensitivity; and obtaining an analysis result of the input image using a classification model that distinguishes the input image into classification classes when the abnormality score is greater than the cut-off score.

Abstract: A medical imaging communication system generates an automated report for a medical image corresponding to a patient. The system receives one or more medical images for a patient. The system applies a model to identify an abnormality in the received one or more medical images. The model is configured to determine a likelihood score for the presence of the abnormality in the one or more medical images. If the likelihood score is above a threshold value, the system generates an automated report indicating the type of abnormality identified in the received one or more medical images. The automated report is then provided to a viewing user (e.g., a medical professional viewing the one or more medical images).

Abstract: In an example method of training a neural network for performing visual odometry, the neural network receives a plurality of images of an environment, determines, for each image, a respective set of interest points and a respective descriptor, and determines a correspondence between the plurality of images. Determining the correspondence includes determining one or point correspondences between the sets of interest points, and determining a set of candidate interest points based on the one or more point correspondences, each candidate interest point indicating a respective feature in the environment in three-dimensional space). The neural network determines, for each candidate interest point, a respective stability metric and a respective stability metric. The neural network is modified based on the one or more candidate interest points.

Abstract: An image diagnosis supporting device includes a model reader that reads an image diagnostic model that outputs a diagnostic result for a diagnostic image that is an input medical image, a storage unit that stores facility data that is a plurality of medical images associated with diagnostic results held in a facility, and an adjuster that adjusts, based on the facility data, the image diagnostic model or the diagnostic image input to the image diagnostic model.

Abstract: A method for detecting road edges at a predetermined intersection, comprising: receiving, by the controller, aerial imagery data about the predetermined intersection; receiving, by the controller, vehicle telemetry data from at least one vehicle passing through the predetermined intersection; detecting, using the aerial imagery data and generative adversarial networks (GANs) executed on the controller, road edges at the predetermined intersection; classifying, using the vehicle telemetry data and a random forest classifier (RFC) executed on the controller, each vehicle trajectory passing through the predetermined intersection with a label corresponding to a unique maneuver to create a maneuver labeling at the predetermined intersection; constructing, using the maneuver labeling determined by the RFC and the road edges, a probabilistic finite state automata (PFSA) to pair inbound lanes with outbound lanes at the predetermined intersection; and determining lane edges at the predetermined intersection using a ho

Abstract: Disclosed are an image processing method and device, a training method of a neural network and a storage medium. The image processing method includes: obtaining an input image, and processing the input image by using a generative network to generate an output image. The generate network includes a first sub-network and at least one second sub-network, and the processing the input image by using the generative network to generate the output image includes, processing the input image by using the first sub-network to obtain a plurality of first feature images; performing a branching process and a weight sharing process on the plurality of first feature images by using the at least one second sub-network to obtain a plurality of second feature images; and processing the plurality of second feature images to obtain the output image.

Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: A nail application causes an information processing apparatus to function as a user information input unit that acquires user information, an image display control unit that acquires image data of a finger inclusive of a nail of a user, and a nail detection unit that detects a nail area based on data indicating the nail area in the image data to be outputted from a nail detection model as a consequence of inputting the image data and the user information to the nail detection model.