Abstract: Disclosed herein are a system and method for assisting the verification of the contouring of medical images. A system for assisting the verification of the contouring of medical images according to an embodiment of the present invention receives a medical image set including a plurality of medical images via the communication interface, receives the result information of contouring performed on a plurality of regions of interest (ROIs) on the plurality of medical images in the medical image set via the communication interface, calculates the feature value of each of the plurality of ROIs, corresponding to the result information of the contouring, for each of the plurality of medical images, and displays the boundary of each of the plurality of ROIs corresponding to the result information of the contouring by projecting the boundary onto a reference plane.

Abstract: To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.

Abstract: A computer-implemented method for correcting artifacts within CT images of a subject includes providing iteratively correcting a CT volumetric image with a high-contrast mask by overpainting the high-contrast mask within CT volumetric image set by evaluating a model function and updating the CT projections to reflect the overpainting using a gradient descent based on previous overpainted CT projections, back-projecting the corrected CT projections to produce a corrected volumetric image set, and comparing the corrected CT volumetric image set against a previous corrected CT volumetric image set until convergence. The original voxels overpainted by the high-contrast mask are re-inserted into the converged CT volumetric image to produce the final CT volumetric image set.

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: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

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: 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: 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: 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: 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: 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: The present disclosure describes ultrasound imaging systems and methods configured to identify ovarian follicles by applying a neural network to pre-processed ultrasound image frames. Systems may include an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target region. One or more processors communicatively coupled with the ultrasound transducer may be configured to generate at least one image from the ultrasound echoes. A threshold may be applied to the image frame that differentiates pixels representative of an ovarian follicle present in the target region from other subject matter. The processors may apply a neural network to the thresholded image frame, in which the neural network determines the presence of the ovarian follicle in the thresholded image frame. The processors also may generate an indicator based on the presence of the ovarian follicle and display the indicator on a user interface.

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.

Abstract: An information acquisition unit acquires at least one information item of subject information, imaging condition information, or positioning information. Next, a body movement feature derivation unit derives body movement feature information indicating a feature of body movement of a breast which occurs at the time of the tomosynthesis imaging. A storage stores correspondence information in which the derived body movement feature information corresponds to at least one information item of the subject information, the imaging condition information, or the positioning information.

Abstract: The invention relates generally to both a method and apparatus for the creation of full resolution color digital images of diagnostic cassettes or objects of interest using a gray-scale digital camera or sensor combined with time sequential illumination using additive primary colors followed by post exposure digital processing. Such procedures and equipment is of significant economic value when employed in situations such as diagnostic clinical analyzers where space is limited and image quality requirements are high.

Abstract: Disclosed are an apparatus and method for assisting the diagnosis of a medical image by an automated system. A computing system includes a memory or database that stores a plurality of medical image diagnosis algorithms each having a medical image diagnosis function. A processor inside the computing system extracts diagnosis requirements for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms to be applied to the diagnose of the medical image from among a plurality of medical image diagnosis algorithms based on the diagnosis requirements, and generates display information including diagnosis results for the image frame by applying the plurality of selected diagnosis application algorithms to the image frame.

Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, and methods for supporting reading of a fundus image of a subject. In certain aspects, disclosed a method including the steps of: extracting a first feature information from a first fundus image of the subject based on a machine learning model; generating a second fundus image having a second feature information by mapping an adversarial factor to the first fundus image so that the first feature information is changed; and displaying the first fundus image having the first feature information and the second fundus image having the second feature information on a display device.

Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.

Abstract: A system (SY) for determining a relative importance of each of a plurality of image features (Fn) of a vascular medical image impacting an overall diagnostic metric computed for the image from an automatically-generated diagnostic rule. A medical kin image database (MIDB) includes a plurality of vascular medical images (M1 . . . k). A rule generating unit (RGU) analyzes the plurality of C vascular medical images and automatically generates at least one diagnostic rule corresponding to a common diagnosis of a subset of the plurality of vascular medical images based on a plurality of image features common to the subset of vascular medical images. An image providing unit (IPU) provides a current vascular medical image (CVMI) including the plurality of image features. A diagnostic metric computation unit (DMCU) computes an overall diagnostic metric for the current vascular medical image by applying the at least one automatically-generated diagnostic rule to the current vascular medical image.

Abstract: Aspects of the technology described herein relate to automatically calculating and displaying a prediction of a collective opinion of a group of individuals regarding imaging data and/or an output based on the imaging data. In some embodiments, the prediction may be a prediction of the collective opinion of a group of individuals regarding the usability of imaging data, regarding a segmentation of an image, or regarding a measurement performed based on the imaging data.