Abstract: Provided is a method for performing image guidance, including: acquiring a 3D magnetic resonance (MR) image of a target individual, wherein at least one region range of an object of interest is marked in the 3D MR image; acquiring a reference 3D image of the target individual, wherein the reference 3D image is a 3D-reconstructed computed tomography (CT) image; performing a 3D-3D registration on the 3D MR image and the reference 3D image, so as to mark each region range of the object of interest in the reference 3D image; and performing image guidance on the basis that the reference 3D image is adopted to characterize an initial position state.

Abstract: An image processing unit includes a grayscale image creating unit, a shininess region removal unit, a region of interest tracing unit, and a time intensity curve measuring unit. The time intensity curve measuring unit includes a pixel value measuring unit which measures the pixel value of a specific point in the fluorescence image photographed by the fluorescence image pickup element, and a measurement position movement unit which moves the measurement position of the pixel value in the pixel value measuring unit in accordance with the region of interest traced by the region of interest tracing unit.

Abstract: A system and method for generating and displaying contours. In some embodiments, the method includes: calculating a first threshold, based on a first target point in a first slice; and generating a first contour, the first contour being a boundary of a first region, the first region being a region within which: the elements of the first slice are greater than the first threshold, or the elements of the first slice are less than the first threshold.

Abstract: This application relates to a method for transmitting a scene image of a virtual scene. The method includes: rendering a first image to obtain a first rendered image, wherein the first image is obtained through acquisition of display data of a virtual scene when the display data changes; acquiring a first time point at which the rendering of the first image is completed; encoding the first rendered image to obtain an encoded image when an interval between the first time point and a second time point is not less than a first interval threshold, wherein the second time point is a time point at which image encoding is performed last time; and transmitting the encoded image to a terminal for decoding and displaying.

Abstract: A computer-implemented method for providing a vascular image data record includes receiving a plurality of projection-X-ray images recorded in temporal succession. The plurality of projection-X-ray images at least partially map a common examination region of an examination object. The plurality of projection-X-ray images map a temporal change in the examination region of the examination object. A change image data record is determined in each case based on at least one region of interest of the plurality of projection-X-ray images. The at least one region of interest includes a plurality of image points. The change image data record in each case includes a time-intensity curve for each of the image points. The vascular image data record is generated based on the change image data record, and the vascular image data record is provided.

Abstract: Embodiments related to methods of use of an image analysis system for identifying residual cancer cells after surgery are disclosed. In some embodiments, a patient-specific threshold used to detect abnormal cells in a surgical site can be determined. A medical imaging device can be configured to produce a set of images of an anatomy of a patient. An image analysis system, comprising one or more processors, can be configured to receive the set of images, and analyze the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

Abstract: A medical image processing apparatus comprises processing circuitry configured to select a reference image from blood vessel images based on contrast images acquired in time series and conduct a transformation process on other blood vessel images such that blood vessel shapes in the other blood vessel images match a blood vessel shape in the reference image, generate a color image where a color that corresponds to a temporal change in a pixel value is assigned to each pixel based on the blood vessel images after the transformation process, and display the color image on a display.

Abstract: A squeeze-enhanced axial transformer (SeaFormer) for mobile semantic segmentation is disclosed, including a shared stem, a context branch, a spatial branch, a fusion module and a light segmentation head, wherein the shared stem produces a feature map; the context branch obtains context-rich information; the spatial branch obtains spatial information; the fusion module incorporates the features in the context branch into the spatial branch; and the light segmentation head receives the feature from the fusion module and output the results. This application is also related to the layer of the SeaFormer, as well as methods thereof.

Abstract: A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

Abstract: Machine learning technologies are used to identify and separating abnormal and normal subjects and identifying possible disease types with images (e.g., optical coherence tomography (OCT) images of the eye), where the machine learning technologies are trained with only normative data. In one example, a feature or a physiological structure of an image is extracted, and the image is classified based on the extracted feature. In another example, a region of the image is masked and then reconstructed, and a similarity is determined between the reconstructed region and the original region of the image. A label (indicating an abnormality) and a score (indicating a severity) can be determined based on the classification and/or the similarity.

Abstract: Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

Abstract: An example method for updating convolutional neural network includes: obtaining a sample with a classification label; performing a first operation on the sample based on parameters of each layer of front-end network, to obtain a first operation result; performing a second operation on the sample based on the first operation result and the parameters of each layer of back-end network that the first GPU has, to obtain a second operation result; separately sending the first operation result to the other GPUs; receiving a third operation result obtained after each other GPU performs a third operation on the sample based on their parameters of each layer of back-end network and the first operation result; combining the second and third operation results to obtain a classification result; determining a prediction error based on the classification result and the classification label; and updating the convolutional neural network based on the prediction error.

Abstract: The disclosure relates to a method for creating a three-dimensional digital subtraction angiography image of a vascular system of a patient. The method includes: providing a first reconstructed three-dimensional filling image which was acquired during at least partial contrast agent filling of the vascular system with a first contrast agent; providing a second reconstructed three-dimensional filling image which was acquired during at least partial contrast agent filling of the vascular system with a second contrast agent; and subtracting the first three-dimensional filling image from the second three-dimensional filling image so that a three-dimensional subtraction angiography image is produced, wherein the first contrast agent and the second contrast agent differ in that one of the two causes increased X-ray absorption and the other causes reduced X-ray absorption relative to a vascular system without contrast agent.

Abstract: The present disclosure relates to a method for configuring a medical device. The method comprises: providing a set of one or more parameters for configuring the medical device. Each parameter of the set has predefined values. A set of values of the set of parameters may be selected from the predefined values. Using the selected values the set of parameters may be set, which results in an operational configuration of the medical device. The medical device may be controlled to operate in accordance with the operational configuration, thereby an operating status of the medical device may be determined. Based on at least the operating status the operational configuration may be maintained or the selecting, setting and controlling may be repeatedly performed until a desired operating status of the medical device can be determined based on the operating statuses resulting from the controlling.

Abstract: A method includes obtaining multiple spatially-displaced input images of a scene based on image data captured using multiple imaging sensors. The method also includes dividing each of the input images into multiple overlapping sub-images. The method further includes generating multiple overlapping sub-image gain maps based on the sub-images. In addition, the method includes combining the sub-image gain maps to produce a final gain map identifying relative gains of the imaging sensors. An adjacent and overlapping pair of sub-image gain maps are combined by renormalizing gain values in at least one of the pair of sub-image gain maps so that average gain values in overlapping regions of the pair of sub-image gain maps are equal or substantially equal.

Abstract: An object detection method for a 3D mammogram is disclosed. The object detection method comprises steps of: controlling N filters to execute a filtering computation in the 3D mammogram respectively to generate N 3D filtering images; computing a difference variation among the plurality of voxels to obtain a blurriness value of the plurality of voxels; using the blurriness value of the plurality of voxels in a decision module to execute a plurality of first decision operators to generate a plurality of first decision results, and using one of the plurality of first decision results to execute the plurality of second decision operators to generate a plurality of second decision results; and executing a final decision operator by using the plurality of first decision results and the plurality of second decision results to generate a detection object of the 3D mammogram.

Abstract: A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Abstract: A system including a camera configured to capture a first image of an offshore structure and a second image of the offshore structure, and a processor coupled to the camera, the processor configured to receive the first image and the second image from the camera, recognize a first visual pattern in the first image and the second image, extract pixel coordinates associated with a first position of the first visual pattern in the first image and a second position of the first visual pattern in the second image, and determine a first distance between the first position and the second position, or the first position of the first visual pattern and a first position of a second visual pattern fixably attached to the offshore structure in the first image.

Abstract: A method for providing an optimum subtraction data set includes: receiving first image data sets acquired by a medical imaging device and which map an object under examination within a first time phase; receiving at least one second image data set acquired by the same or another medical imaging device and which maps a change in the object under examination within a second time phase; dividing the at least one second image data set into a plurality of image regions; generating subtraction image regions for the plurality of image regions; determining an image quality parameter for each subtraction image region; determining an optimum subtraction image region for each image region of the plurality of image regions of the at least one second image data set by comparing the image quality parameters; generating the optimum subtraction data set from the optimum subtraction image regions; and providing the optimum subtraction data set.

Abstract: The present application provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises obtaining an original image acquired by an X-ray imaging system, and post-processing the original image based on a trained network to obtain an optimized image after processing.

Abstract: Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Abstract: A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory of a target. The system estimates this trajectory in two stages: a first stage in which the range and Doppler measurements from the lidar system along with various feature measurements obtained from the images from the video system are used to estimate first stage motion aspects of the target (i.e., the trajectory of the target); and a second stage in which the images from the video system and the first stage motion aspects of the target are used to estimate second stage motion aspects of the target. Once the second stage motion aspects of the target are estimated, a three-dimensional image of the target may be generated.

Abstract: Systems and methods for processing electronic images from a medical device comprise receiving a first image frame and a second image frame from a medical device, and determining a region of interest by subtracting the first image frame from the second image frame, the region of interest corresponding to a visual obstruction in the first image frame and/or second image frame. Image processing may be applied to the first image frame and/or second image frame based on a comparison between a first area of the first image frame corresponding to the region of interest and a second area of the second image frame corresponding to the region of interest, and the first image frame and/or second image frame may be provided for display to a user.

Abstract: A method of performing a procedure on a breast of a patient includes compressing the breast of the patient with a paddle of an imaging system and performing an initial imaging procedure on the breast of the patient. The initial imaging procedure includes imaging the breast with an x-ray source at a first energy to obtain a first image and a second energy to obtain a second image. A composite image is generated from the first image and the second image and displayed. A region of interest on the composite image is targeted and a biopsy is performed.

Abstract: A method and a device are for detecting an anatomical feature of a section of a blood vessel. In an embodiment, the device includes a first interface embodied to receive a medical image data record, including a region of an anatomy of a patient to be examined, which has the section of a blood vessel; a first computing unit, embodied to apply a first algorithm pretrained on an anatomical feature for detecting the anatomical feature based upon the medical image data record; and a second interface embodied to provide an item of information based upon the detection of the anatomical feature. The anatomical feature includes a change in caliber of a blood vessel. Further, the presence of the anatomical feature is indicative of vascular pulmonary disease, in particular of chronic thromboembolic pulmonary hypertension.

Abstract: According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry specifies, before position alignment between a first X-ray image and a second X-ray image which is acquired with a device inserted, a device area candidate in the second X-ray image as a candidate of an area where the device appears. The processing circuitry performs the position alignment using first processing of removing the specified device area candidate or second processing of reducing a contribution of the specified device area candidate.

Abstract: An image processing system and method obtains one or more source images in which a damaged vehicle is represented, and performs one or more image processing techniques on the obtained images to determine, predict, estimate, and/or detect damage that has occurred at various locations on the vehicle. Specifically, the system/method generates damage parameter values based on the image-processed images, where each value corresponds to damage at a particular location on the vehicle. The image processing techniques may include generating a composite image of the damaged vehicle, aligning and/or isolating the image, applying convolutional neural network techniques to the image to generate the damage parameter values, etc. Based on the generated values, the image processing system/method determines, predicts, estimates, and/or selects a set of parts that are needed to repair the vehicle and, in some embodiments, orders the parts needed to repair the vehicle.

Abstract: Vasculature modeling systems and methods are disclosed that generate an enhanced 3D model based on a combination of two dimensional, 2D, imaging data of a region of interest and 3D imaging data of the region of interest. A hemodynamic simulation is performed using the enhanced 3D model to derive at least one hemodynamic parameter based on the hemodynamic simulation.

Abstract: A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.

Abstract: A work machine includes a control device including a lead position detecting section to detect a lead position which is positions of a pair of leads based on imaged data imaged by an imaging device, a first detecting region setting section to set, based on the lead position detected by the lead position detecting section, a first detecting region outside the pair of leads including a part of a bottom section outside the pair of leads, a second detecting region outside the pair of leads, being opposed to the first detecting section across the pair of leads including a part of the bottom section, and a polarity determining section configured to determine the polarity of the pair of leads based on whether the mark portion exists in each of the first detecting region and the second detecting region.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: A sequence of video frames of an area of interest is obtained. A first background model of the area of interest is constructed based on a first parameter. A second background model of the area of interest is constructed based on a second parameter, the second parameter being different from the first parameter. A difference between the first and second background models is determined. A stationary target is determined based on the determined difference. An alert concerning the stationary target is generated.

Abstract: An ultrasound device (18) is arranged to acquire an ultrasound image (19) of a thoracic diaphragm. A current location (24) of a tumor is determined using the ultrasound image (19) of the thoracic diaphragm and a predetermined relationship (14) assigning tumor locations to a set of simulation phase ultrasound images of the thoracic diaphragm in different geometries, or using a predetermined relationship (114) assigning tumor locations to a set of meshes representing the thoracic diaphragm in different geometries. The tumor may, for example, be a lung tumor.

Abstract: A system and method for retrieving a blood vessel corresponding positional relation under multi-angle angiography. The system includes an angiography machine, an angiographic image receiving module, a center point calibration module, a corresponding feature point detection module, and a feature point corresponding blood vessel module. The angiography machine is used for performing angiography and collecting angiographic images of multiple projection body positions. The angiographic image receiving module is used for receiving an angiographic image and transmitting the angiographic image to the center point calibration module. The center point calibration module is used for calibrating a center point offset caused by the multi-angle angiography system and transmitting the result to the feature point detection module.

Abstract: A method of determining a risk probability of vertebral artery dissection (VAD) in a patient, including receiving medical image information of the patient and clinical report information of the patient; extracting at least one biomarker corresponding to a vertebral artery segment included in the medical image information; extracting patient history information from the clinical report information; and determining the risk probability of VAD using a deep learning classification model based on the extracted at least one biomarker and the extracted patient history information.

Abstract: A method for computing fractional flow reserve (FFR), including receiving geometrical parameters of a blood vessel segment including a proximal end and a distal end, the geometrical parameters including a first geometrical parameter, a second geometrical parameter and a third geometrical parameter; and with the proximal end as a reference point, deriving a reference lumen diameter function and a geometrical parameter difference function based on the geometrical parameters and the distance from the position along the segment of blood vessel to the reference point. Derivatives of the geometrical parameter difference function are calculated in multiple scales. FFR is computed as a ratio of a second blood flow pressure at the first location of the blood vessel to a first blood flow pressure at the proximal end of the segment based on the multiple scales of derivative difference functions and the maximum mean blood flow velocity.

Abstract: A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Abstract: An image processing system (IPS) and related method. The system comprises an input interface (IN) for receiving an earlier input image (Va) and a later input image (Vb) acquired of an object (OB) whilst a fluid is present within the object (OB). A differentiator (?) is configured to form a difference image (Vd) from the at least two input images (Va,Vb). An image structure identifier (ID) is operable to identify one or more locations in the difference image. Based on a respective feature descriptor that describes a respective neighborhood around said one or more locations. An output interface (OUT) outputs a feature map (Vm) that includes the one or more locations.

Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Abstract: For classifying magnetic resonance image quality or training to classify magnetic resonance image quality, deep learning is used to learn features distinguishing between corrupt images base on simulation and measured similarity. The deep learning uses synthetic data without quality annotation, allowing a large set of training data. The deep-learned features are then used as input features for training a classifier using training data annotated with ground truth quality. A smaller training data set may be needed to train the classifier due to the use of features learned without the quality annotation.

Abstract: A method for post processing and displaying a three-dimensional angiography image data set of a blood vessel tree of a patient, wherein two-dimensional display images are rendered from the angiography image data set and displayed, wherein two display images are rendered from the angiography image data set using viewing directions forming an angle suited for stereoscopic perception of the display images and both display images are simultaneously displayed on a display screen in a display presentation that causes each display image to be viewed by one eye of a person viewing the display screen.

Abstract: Methods, systems and apparatuses of feature extraction and object detection are provided. In the method of feature extraction, a plurality of image channels are generated from each of training images; intra-channel features are extracted from the plurality of image channels for each of training images, wherein the intra-channel features include features independently extracted from a single image channel; cross-channel features are extracted from the plurality of image channels for at least one of the training images, wherein the cross-channel features include features extracted from at least two image channels. The intra-channel features and the cross-channel features form a set of features for feature selection and classifier training.

Abstract: Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

Abstract: The invention relates to x-ray imaging technology as well as image post-processing. Particularly, the present invention relates to post-processing of perfusion image data acquired by an x-ray imaging apparatus by absolutely or relatively normalizing perfusion image data to allow a preferred comparison of the image data, both with regard to different acquisitions as well as different patients. To allow normalization of perfusion image data, it may be desirable to know the amount of contrast agent injected, which remains in a coronary. Subsequently, image parameters may be adapted or normalized based on the known amount of contrast agent within the coronary for normalization of perfusion image data. To obtain a precise amount of injected contrast agent, the injected volume of contrast agent flowing through a defined region or section of a vessel may be estimated. Said injected volume of contrast agent may thus be deduced from the estimation of the total volume flow at this location.

Abstract: An X-ray imaging apparatus includes an X-ray generator configured to generate X-rays and radiate the X-rays to an object; an X-ray detector configured to detect the X-rays that have passed through the object and convert the X-rays into a signal; and a controller configured to generate a single energy X-ray image and multiple energy X-ray images including a first X-ray image generated using an X-ray having determined energy band that is extracted from the X-rays detected by the X-ray detector. The multiple energy X-ray images further include a second X-ray image in which a specific substance is separated, which is image is generated using the image signals corresponding to the energy bands from the detected X-rays, respectively, and the controller is further configured to control a display to display the single energy X-ray image and the first X-ray image and the second X-ray image on a screen of the display.

Abstract: A method and apparatus are provided for generating enhanced digital imagery from digital input images (7) exhibiting differences other than or in excess of differences in projection. By applying a plurality of image transformations (10) to frequency normalised (8) versions of the input images, and generating measures of image similarity (11) therefrom, an optimum image transformation (12) can be determined that can be applied to the digital input images such that they are substantially matched. Digital input images of physical objects or environments to which the method is applied can then be used in image fusion, ortho-rectification or change detection applications, in order to monitor the physical object or environment.

Abstract: A method is for performing an angiographic measurement and creating an angiogram of a body region of a patient in a magnetic-resonance system and a magnetic-resonance system for operating such a method.

Abstract: An apparatus for determining an enhanced image of a vascular treatment provides (12) a plurality of images including a representation of a region of interest of a vascular structure. Each of the plurality of images includes image data of at least one localizing feature associated with at least one tool configured to be used in the vascular treatment. Each of the plurality of images also includes image data associated with the at least one tool. Registration information for each of the images of the plurality of images is determined (14). At least two images from the plurality of images are selected (16) as a function of the registration information for each of the images. An enhanced image that provides for enhanced visibility of the at least one tool is determined (20). Data representative of the enhanced image is output (24).