Abstract: A CPU acquires a radiographic image obtained by imaging an imaging region where a patient is present, with a radioscopy apparatus using radiation emitted from a radiation source and applied to an irradiation field adjusted by a collimator. The CPU specifies a structure image that is included in the radiographic image and represents a structure of a specific shape having transmittance of radiation lower than the patient, based on the specific shape. The CPU executes image processing corresponding to the structure image on a patient image region imaged by a radioscopy apparatus in a case where an irradiation field excluding a position of the structure is set as the irradiation field, in a region in the radiographic image.

Abstract: Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.

Abstract: An embodiment relates to a computer-implemented method for generating a combined tissue-vessel representation. The method includes receiving imaging data of a tissue; receiving imaging data of a vessel; generating a tissue representation based on the imaging data of the tissue; generating a vessel representation based on the imaging data of the vessel; and generating a combined tissue-vessel representation based on the vessel representation and the tissue representation, the vessel representation being overlaid over the tissue representation.

Abstract: Disclosed is an image judgment device including a hardware processor that functions as an image acquirer that acquires image data of a medical image that is obtained by radiographing of a part including a region to be diagnosed of a patient, a segmentation unit that extracts an anatomical structure from the medical image to generate an extracted image, an image converter that performs image conversion on the extracted image to generate a converted image; a feature value extractor that calculates a feature value from the extracted image and/or the converted image by using a result of machine learning; and a judgment unit that judges whether the medical image is taken with positioning appropriate for diagnosis based on the feature value by using a result of machine learning concerning feature values.

Abstract: Provided is a method for improving reliability of artificial intelligence-based object recognition, in which: in a server interworking with an artificial intelligence module, one or more object regions included in learning data including an image of a recognition target object to be recognized through the artificial intelligence module are recognized; object region recognition data which sets the recognized object regions is extracted and is provided to user terminals in a designated order; a procedure for receiving, from the user terminals, object region selection data which selects at least one effective object region corresponding to the recognition target object among object regions included in the object region recognition data is performed; and the object region selection data of respective users received from the user terminals is mutually compared and analyzed by the same object region selection data.

Abstract: The present disclosure relates to systems and methods for visualizing a guidewire in a roadmap image. In accordance with certain embodiments, a method includes generating a background mask as a function of at least one background image, wherein the at least one background image includes a blood vessel of interest, generating a contrast mask as a function of at least one contrast image, wherein the at least one contrast image includes a contrast within the blood vessel of interest, receiving a guidewire image, wherein the guidewire image includes a guidewire within the blood vessel of interest, generating a roadmap image as a function of the background mask, contrast mask, and guidewire image, wherein the roadmap image includes the blood vessel of interest, and the guidewire, and outputting the roadmap image to a display.

Abstract: Disclosed are systems, methods, and computer program products for tumor lesion identification, segmentation, tracking and analysis, wherein a 3D spatial distribution of the tumor lesions in a target organ forms a unique 3D point structure for a patient.

Abstract: Provided are a method, an apparatus, and a device for identifying human body, including: acquiring a first original picture captured; adjusting a resolution according to the acquired picture to obtain a target picture; processing the target picture based on a preset model for human body feature point detection to determine whether the target picture includes human body information; if the target picture includes the human body information, determining human body area information in the original picture according to the human body information and inputting the human body area information into a filter, enabling that the filter determines target human body area information according to the human body area information; acquiring a next original picture captured; and determining a possible human body area in the next original picture according to the target human body area information, and performing the step of adjusting the resolution according to the possible human body area.

Abstract: Embodiments of the present disclosure provide an image recognition method and apparatus, an electronic device and a medium, and relates to the field of image processing. The method may include: acquiring a to-be-recognized image sent by a client, and determining whether the to-be-recognized image is a back image; determining characteristic information of the to-be-recognized image in at least one dimension, when it is determined that the to-be-recognized image is the back image; and determining a target front side image matching the to-be-recognized image according to the characteristic information, and returning the target front side image to the client to cause the client to display the target front side image.

Abstract: An annotation method and device and a storage medium are provided. The annotation method includes operations as follows. A first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag is determined based on first tag information of a first image set. M and N are unequal and are positive integers. The first probability value is added to second tag information of a second sample image annotated with the Mth tag in a second image set.

Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.

Abstract: A system and method for metal artifact avoidance in 3D x-ray imaging is provided. The method includes determining a 3D location of metal in an object or volume of interest to be scanned; estimating a source-detector orbit that will reduce the severity of metal artifacts; moving an imaging system to locations consistent with the source-detector orbit that was estimated; and scanning the object according to the source-detector orbit.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: A system and a method for determining an object has been on a vehicle seat of a vehicle are provided. The processing circuitry may determine if passengers of the vehicle have left the vehicle. The processing circuitry may determine if a temperature of a vehicle cabin of the vehicle meets a threshold of temperature. The temperature of the vehicle cabin to a first temperature may be adjusted. A temperature of resistive filaments of the vehicle seat of the vehicle to a second temperature may be adjusted. A thermal image of the vehicle seat with a reference thermal image of the vehicle seat may be compared. The processing circuitry may determine if the object has been left on the vehicle seat.

Abstract: An image processing apparatus includes an extraction portion and a fusion portion. The extraction portion extracts a first object from each of a plurality of pieces of image data that include the first object and a second object, the first object including a handwritten object, the second object including a non-handwritten object. The fusion portion generates a fusion image by fusing a plurality of first objects extracted from the plurality of pieces of image data into the second object that is common to the plurality of pieces of image data.

Abstract: A system configured to reduce false positives when performing human presence detection is provided. In addition to calculating a Human Detection (HD) confidence score during human presence detection, the system may use human keypoint detection (HKD) techniques to calculate a true positive (TP) confidence score and detect false positives based on a combination of the two confidence scores. For example, the device system may generate keypoint data, which indicates a location and maximum confidence value for individual keypoints associated with a human body. The system may input the keypoint data to a model configured to generate the TP confidence score, such as a logistic regression model that is configured to receive numerical values as inputs (e.g., HD confidence score and 17 keypoint confidence values) and generate the TP confidence score. The system then detects false positives using the TP confidence score and may remove corresponding bounding boxes.

Abstract: The present invention relates to a method for supporting reading of a fundus image of a subject, and a computing device using the same. Specifically, the computing device according to the present invention acquires the fundus image of the subject, extracts attribute information from the fundus image on the basis of a machine learning model for extracting the attribute information of the fundus image, and provides the extracted attribute information to an external entity. In addition, when evaluation information on the extracted attribute information or modification information on the attribute information is acquired, the computing device according to the present invention can also update the machine learning model on the basis of the acquired evaluation information or modification information.

Abstract: According to one embodiment, a task assignment supporting apparatus includes processing circuitry. The processing circuitry manages types of image reading preferences indicating types of image reading tasks preferred by each radiologist. The processing circuitry acquires an image reading order and a type of image reading order indicating a type of image reading task required for the image reading order. The processing circuitry assigns to the image reading order a radiologist with a type of image reading preference corresponding to the type of image reading order.

Abstract: A mobile device includes a rangefinder to measure ranges to points external to the device; a tracking sensor; and a controller connected with the rangefinder and the tracking sensor, the controller configured to: track successive poses of the mobile device in a frame of reference via the tracking sensor; responsive to a first activation of the rangefinder at a first one of the poses, generate a first position, in the frame of reference, of a first external point based on a first range from the rangefinder and the first pose; responsive to a second activation of the rangefinder at a second one of the poses, generate a second position, in the frame of reference, of a second external point based on a second range from the rangefinder and the second pose; and determine a distance between the first and second external points based on the first and second positions.

Abstract: Systems, instruments, and methods for medical treatment are disclosed. The methods comprise, by a computing device: receiving information identifying at least one first point on a body part shown in a medical image; overlaying a first mark on the medical image for the at least one first point; generating a spline based at least on the first mark; overlaying a second mark for the spline on the medical image; identifying a location of at least one second point on the body part shown in the medical image based on the first and second marks; overlaying a third mark for the at least one second point on the medical image; and using at least the third mark to facilitate the medical treatment of an individual whose body part is shown in the medical image.