Abstract: An image processing apparatus selects one or a plurality of examinations to which a medical image belongs, determines image processing candidate examinations based on the selected one or plurality of examinations, displays medical images belonging to the determined image processing candidate examinations on a display unit, and executes image processing using, of the displayed medical images, a plurality of medical images selected by a user, wherein, when the one examination is selected, the selected one examination and one or a plurality of examinations obtained by a search based on the selected one examination are determined as the image processing candidate examinations, and when the plurality of examinations are selected, in the determining, the selected plurality of examinations are determined as the image processing candidate examinations.

Abstract: Systems and methods are disclosed for performing operations comprising: receiving first and second images depicting an anatomy of a subject; obtaining a segmentation associated with the first image; applying a trained neural network to estimate the adapted segmentation corresponding to the anatomy depicted in the second image, the trained network consisting of three sub-networks: a registration sub-network, generating an initial segmentation estimate representing a deformation of the segmentation associated with the first image to fit the anatomy depicted in the second image, a segmentation sub-network, generating a second initial segmentation estimate for the second image, and a third refinement sub-network, combining the two initial segmentations and generating a refined segmentation for the second image.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Abstract: The present disclosure is related to a method for scattering correction of an image. The method may include obtaining an image of a subject and a reference image of air. The method may also include identifying an OOI from the image, the OOI including one or more pixels. For each pixel of the one or more pixels of the OOI, the method may also include determining an equivalent thickness of the OOI corresponding to the each pixel based on a pixel value of the each pixel and the reference image, and determining a scatter correction coefficient of the each pixel based at least in part on the equivalent thickness of the OOI corresponding to the each pixel. The method may further include correcting the pixel value of the each pixel using the corresponding scatter correction coefficient for each pixel of the one or more pixels of the OOI.

Abstract: A plurality of image projections are acquired at faster than cardiac rate. A spatiotemporal reconstruction of cardiac frequency angiographic phenomena in three spatial dimensions is generated from two dimensional image projections using physiological coherence at cardiac frequency. Complex valued methods may be used to operate on the plurality of image projections to reconstruct a higher dimensional spatiotemporal object. From a plurality of two spatial dimensional angiographic projections, a 3D spatial reconstruction of moving pulse waves and other cardiac frequency angiographic phenomena is obtained. Reconstruction techniques for angiographic data obtained from biplane angiography devices are also provided herein.

Abstract: A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images.

Abstract: Disclosed is a computer-implemented method of determining a correspondence between a region of interest as it appears in a first digital medical patient image and as it appears in a second digital medical image. The correspondence is determined by calculating the ratio of overlap of the region of interest with a data object defining an anatomical body part in the first image and the second image and determining whether the larger of the two ratios exceeds a threshold. If the threshold is exceeded, the method assumes that the appearances in the two images describe the same region of interest.

Abstract: Face image data is acquired and a face image is captured, and a difference between a face image indicated by the face image data and the face image that is captured or a candidate of the difference is detected on the basis of at least one of the face image indicated by the face image data that is acquired, and the face image that is captured. Guidance is acquired on the basis of the difference or the candidate of the difference which is detected, and an output unit is controlled to output the guidance.

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: A method for analysis of spine anatomy and stenosis is disclosed herein. The method includes preprocessing (3203) of images and then running segmentation models for each area of interest such as the foramen, disc, canal, vertebra (3206). In image post processing (3207), the system runs various heuristics to ensure accuracy (3208), computes areas (3209), and then runs a comparison model (3210). The report template produces HTML that is then converted to a PDF file of the final report (3214).

Abstract: A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

Abstract: An image acquisition unit acquires two radiographic images based on radiations which are transmitted through a subject containing a plurality of compositions and have energy distributions different from each other. A body thickness derivation unit derives, as a first body thickness and a second body thickness, body thicknesses of the subject for pixels of the two radiographic images. A composition ratio derivation unit derives composition ratios of the subject for the pixels of the radiographic images based on the first body thickness and the second body thickness.

Abstract: Embodiments include controlling a processor to access an image of a region of tissue demonstrating cancerous pathology; segment a cellular nucleus represented in the image; extract a first set of features from the segmented cellular nucleus; classify the segmented nucleus as a lymphocyte or non-lymphocyte based on the first set of features; for a segmented nucleus classified as a lymphocyte: computing a set of contextual features; assign the segmented nucleus classified as a lymphocyte to one of a plurality of clusters based on the set of contextual features; compute a frequency distribution of the clustered segmented nuclei classified as lymphocytes; provide the frequency distribution to a machine learning classifier; receive, from the machine learning classifier, a classification of the region of tissue as likely to experience recurrence or unlikely to experience recurrence, based, at least in part, on the frequency distribution; and display the classification.

Abstract: Systems and methods for replacing original media bookmarks of at least a portion of a digital media file with replacement bookmarks is described. A media fingerprint engine detects the location of the original fingerprints associated with the portion of the digital media file and a region analysis algorithm characterizes regions of media file spanning the location of the original bookmarks by data class types. The replacement bookmarks are associated with the data class types and are overwritten or otherwise are substituted for the original bookmarks. The replacement bookmarks then are subjected to a fingerprint matching algorithm that incorporates media timeline and media related metadata.

Abstract: A display control device including a display and a processor configured to acquire a first image of an object and a second image of the same object as the first image; display the first image including one or more of first regions of interest and the second image including one or more of second regions of interest on the display; receive designation of a position in the first image displayed on the display; specify a designated first region of interest and a designated second region of interest corresponding to the designated first region of interest on the basis of the designation of the position, wherein, in a case in which the processor receives the designation of the position with a state that a plurality of first regions of interest or a plurality of second regions of interest are present in at least one of the first image or the second image, the processor is further configured to display the designated second region of interest so as to be highlighted on the display.

Abstract: The medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to acquire contrast image data generated by imaging a subject. The processing circuitry is configured to input the acquired contrast image data to a learned model to generate a time phase data classified according to a contrast state of a lesion area with a contrast agent included in the acquired contrast image data, the learned model being for generating the time phase data based on the acquired contrast image data.

Abstract: The present invention relates to a novel methodology for the field of brain images from Positron Emission Tomography (PET), likewise applicable to brain images from Single-Photon Emission Computer Tomography (SPECT) or another technique that makes it possible to generate images of brain metabolism, function or blood flow. This methodology arises from the need to improve the medical diagnosis of neurological disorders and from the need for objective, unbiased quantification of brain metabolism. The method is based on the spatial symmetry of the metabolism of the healthy human brain. Under this assumption, the entire healthy brain metabolism can be deduced from the metabolism of a single hemisphere of the brain.

Abstract: Differential modulation schemes encode a data channel within host signal or noisy environment in a manner that is robust, flexible to achieve perceptual quality constraints, and provides improved data capacity. Differential arrangements enable a decoder to suppress host signal or other background signal interference when detecting, synchronizing and extracting an encoded data channel. They also enable the incorporation of implicit or explicit synchronization components, which are either formed from the data signal or are complementary to it.

Abstract: The medical information display apparatus includes a first data acquisition unit that acquires a table showing relevance between at least one item included in clinical diagnostic information on dementia and a brain area, a second data acquisition unit that acquires clinical diagnostic information on dementia of a subject, an image acquisition unit that receives an input of a three-dimensional brain image, a brain area division unit that divides the three-dimensional brain image of the subject, an image analysis unit that calculates an analysis value for each brain area, an operation unit that selects one item among items included in the clinical diagnostic information of the subject, a display unit, and a display controller that specifies a brain area corresponding to the one item selected by the operation unit and displays brain area specifying information for specifying the specified brain area and an analysis value of the brain area.

Abstract: A method and apparatus is provided to improve the image quality of images generated by analytical reconstruction of a computed tomography (CT) image. This improved image quality results from a deep learning (DL) network that is used to filter a sinogram before back projection but after the sinogram has been filtered using a ramp filter or other reconstruction kernel.