Abstract: Medical imaging methods for processing a three-dimensional (3D) image data set with two-dimensional X-ray images from an X-ray machine using a target function. Methods can include providing a 3D image data set of at least one examination zone in which anatomical structures are present, segmenting the image data set to provide a 3D vascular structure model and a 3D bone structure model, recording a first two-dimensional (2D) X-ray image containing at least a portion of the vascular structure and at least a portion of the bone structure, recording a second 2D X-ray image of the examination zone at a different contrast agent concentration, and subtracting the first and second 2D X-ray images to generate a subtraction image. An optimum projective geometry may then be determined using a three-part target function based on the 3D image data and the 2D X-ray images.

Abstract: A method and system is provided for recording a health care event, optimizing medical procedures and calculating reimbursement. The method includes: acquiring metadata comprising a patient identifier, a practitioner identifier, a health care site identifier, an entry time and a medical reason for the health care event; receiving a reimbursement request; generating a procedures list based on the medical reason; selecting a procedure; generating a list of required data types and a list of required quality data for the procedure; acquiring raw data comprising the procedure, a medical device identifier, an entry time and one or more quality data from the medical device for the procedure; and calculating a reimbursement for the health care event based on the procedure, the medical device identifier, the required quality data and the quality data from the medical device. The method implements iterative learning using the collected data to determine optimal health care procedures.

Abstract: A common area derivation unit derives a common area common to at least a part of a plurality of projection images corresponding to a plurality of radiation source positions which are generated by causing an imaging apparatus to perform tomosynthesis imaging in which a radiation source is moved relative to a detection unit to irradiate a subject with radiation at the plurality of radiation source positions according to movement of the radiation source. A display control unit emphasizes effective image areas, corresponding to the common area, of at least a part of a plurality of tomographic images representing a plurality of tomographic planes of the subject which are generated by reconstructing the plurality of projection images, or a composite two-dimensional image generated from the plurality of tomographic images, and displays at least the part of the tomographic images or the composite two-dimensional image on a display.

Abstract: The present disclosure generally relates to user interfaces for adjusting simulated image effects. In some embodiments, user interfaces for adjusting a simulated depth effect is described. In some embodiments, user interfaces for displaying adjustments to a simulated depth effect is described. In some embodiments, user interfaces for indicating an interference to adjusting simulated image effects is described.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: An image processing method includes image inputting, auxiliary information inputting, image processing, feature quantity extracting, and discriminating. The image inputting is inputting a cell image. The auxiliary information inputting is inputting auxiliary information on a color of the cell image. The image processing is generating multiple processed images by performing a different image-processing process on the cell image based on the auxiliary information. The feature quantity extracting is extracting a feature quantity of a discrimination target from each of the multiple processed images. The discriminating is discriminating the discrimination target in the cell image based on the feature quantity.

Abstract: An illustrative system constructs a map that associates pixel locations in a panoramic image to camera modules in a set of camera modules. The map may associate a first pixel location in the panoramic image to a first camera module in the set of camera modules and a second pixel location in the panoramic image to a second camera module in the set of camera modules. The second pixel location borders the first pixel location in the panoramic image. The system blends, based on at least one weight, a pixel value of the first pixel location captured by the first camera module with a pixel value of the second pixel location captured by the second camera module to determine a pixel value for a blended pixel in the panoramic image.

Abstract: Embodiments of the disclosure provide systems and methods for biomedical image analysis. A method may include receiving a plurality of unannotated biomedical images, including a first image and a second image. The method may also include determining that the first image is in a first view and the second image is in a second view. The method may further include assigning the first image to a first processing path for the first orientation. The method may additionally include assigning the second image to a second processing path for the second view. The method may also include processing the first image in the first processing path in parallel with processing the second image in the second processing path. The first path may share processing parameters with the second path. The method may further include providing a diagnostic output based on the processing of the first image and the second image.

Abstract: A method, system and computer readable storage media for segmenting individual intra-oral measurements and registering said individual intraoral measurements to eliminate or reduce registration errors. An operator may use a dental camera to scan teeth and a trained deep neural network may automatically detect portions of the input images that can cause registration errors and reduce or eliminate the effect of these sources of registration errors.

Abstract: An image viewer and method for using the same in a medical image management system are described.

Abstract: An imaging element outputs a display image with a low pixel count obtained as a result of thinning pixels included in the imaging element during the exposure period of a recording image with a high pixel count. A recording image correction unit receives an input of the recording image from a frame memory after an elapse of the exposure period, and corrects the pixel value of a defective pixel caused by the output of the display image from the imaging element, among pixels included in the recording image, by using the value of addition of the pixel values of display images output during the exposure period of the recording image, so that the pixel value of the defective pixel is corrected to the similar pixel value as that in the case of performing an exposure process for the exposure period of the recording image.

Abstract: A main image includes a tomographic image and a reference image as ultrasonic images. A sub-image includes a site-of-interest map and a site-of-interest list. During execution of ultrasonic inspection, each time a new site of interest is identified, a site-of-interest symbol is added to the site-of-interest map and a site-of-interest record is added to the site-of-interest list. The position of the site-of-interest symbol is determined according to the position of a probe mark.

Abstract: Systems and methods for a pre-treatment quality assurance (QA) of a radiotherapy device may be provided. The method may include determining a measured dose image through an electronic portal dose imaging device (EPID). The method may include determining an energy fluence distribution map related to radiation beams predicted by a first portal dose prediction model. The method may include determining a predicted dose image based on the energy fluence distribution map and a simulated energy response curve related to the EPID. The method may further include determining differences between the measured and predicted dose images by comparing the dose distributions of the measured and predicted dose images.

Abstract: An example method includes acquiring a first image using a medical imaging modality that includes a two-dimensional field of view to include a patient and a multi-modal marker. A second image is acquired using the medical image modality. The second image includes the patient and the multimodal marker and is along a non-coincident angle with respect to the first image. Predetermined portions of the multi-modal marker are visible in the first and second images and have a known location and orientation with respect to at least one sensor detectable by a tracking system. The method also includes estimating a three-dimensional position for predetermined portions of the multi-modal marker. The method also includes determining an affine transformation for registering a three-dimensional coordinate system of the tracking system with a three-dimensional coordinate system of the medical imaging modality.

Abstract: A radiation imaging apparatus includes an imaging area including a plurality of conversion elements configured to convert a radiation into an electrical signal, a detection element provided in the imaging area and configured to detect the radiation, a reading unit configured to read signals of the conversion elements and the detection element, and a control unit configured to execute radiation exposure amount control in capturing a radiation image by a control method selected from a first control method and a second control method based on an imaging condition in capturing the radiation image, the first control method controlling an exposure amount based on the reading result of the detection element read by the reading unit during radiation irradiation, the second control method controlling the exposure amount based on a pixel value of the radiation image, the radiation image being based on the signals of the plurality of conversion elements.

Abstract: An information processing apparatus includes a reception unit configured to receive an input specifying a position of a detection target included in an image, an acquisition unit configured to acquire a storage amount of training data including a pair of information indicating the image and information indicating the position specified by the input, a training unit configured to train a training model to detect the detection target from the image based on the stored training data, and a display control unit configured to control a display unit to display the storage amount, and a reference amount of the training data set as an amount of the training data necessary for the training unit to train the training model, in a comparable way.

Abstract: An image processing method is provided, including: obtaining a target image; invoking an image recognition model including: a backbone network, a pooling module and a dilated convolution module that are connected to the backbone network and that are parallel to each other, and a fusion module connected to the pooling module and the dilated convolution module; performing feature extraction on the target image by extracting, using the backbone network, a feature map of the target image, separately processing, using the pooling module and the dilated convolution module, the feature map, to obtain a first result outputted by the pooling module and a second result outputted by the dilated convolution module, and fusing the first result and the second result by using the fusion module into a model recognition result of the target image; and determining a semantic segmentation labeled image of the target image based on the model recognition result.

Abstract: A system and method for constructing fluoroscopic-based three-dimensional volumetric data of a target area within a patient from two-dimensional fluoroscopic images acquired via a fluoroscopic imaging device.

Abstract: Shadows caused by opacities and obstructions (such as blood vessels) can block detection of structural information of objects below the shadow-causing structure, affecting visualization and analysis of those deeper structures. An image processing method for removing these shadows includes applying a low pass filter to an energy profile of an image to remove high frequency fluctuations in energy, thereby effectively recovering lower energy inside shadow regions and removing high frequency speckle. The image is then adjusted, for example by linear scaling, based on the filtered energy profile. Two dimensional filters may also be applied for volumes.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: A method of providing a dataset for additive manufacturing includes collecting a first type of data for the dataset during the additive buildup of a at least one layer of a component to be manufactured, evaluating of the structural quality of the layer by the first type of data, modifying the first type of data in that fractions of the data representing an insufficient structural quality of the layer are deleted from the first type of data, and superimposing second type of data, to the first type of data, wherein the second type of data is suitable to support a validation of the structural quality of the as-manufactured component.

Abstract: A medical image display device includes a hardware processor that (i) acquires an original image and an analyzed image obtained by analyzing the original image from another device, (ii) carries out predetermined image processing on one of the original image and the analyzed image as acquired, or carries out different types of image processing respectively on the original image and the analyzed image, (iii) superimposes the analyzed image as acquired or a processed analyzed image obtained by carrying out image processing on the analyzed image on the original image as acquired or a processed original image obtained by carrying out image processing on the original image to generate a processed superimposed image, and (iv) causes the superimposed image to be displayed on a display.

Abstract: Provided is an image sensing apparatus including an image sensor including a pixel array configured to output a raw image having a Bayer pattern, and an analog end configured to perform an analog binning process on groups of pixels of same colors included in same columns of each of a plurality of sub-kernels corresponding to a first green pixel, a red pixel, a blue pixel, and a second green pixel, and output median values for different colors, and a digital signal processor configured to perform a digital binning process on the median values for the different colors included in different columns of each of the plurality of sub-kernels, and output a binned image.

Abstract: A method for computing dimensions of an object in a scene includes: controlling, by a processor, a depth camera system to capture at least a frame of the scene, the frame including a color image and a depth image arranged in a plurality of pixels; detecting, by the processor, an object in the frame; determining, by the processor, a ground plane in the frame, the object resting on the ground plane; computing, by the processor, a rectangular outline bounding a projection of a plurality of pixels of the object onto the ground plane; computing, by the processor, a height of the object above the ground plane; and outputting, by the processor, computed dimensions of the object in accordance with a length and a width of the rectangular outline and the height.

Abstract: A multi-model medical scan analysis system is operable to generate a plurality of training sets from a plurality of medical scans. Each of a set of sub-models can be generated by performing a training step on a corresponding one of the plurality of training sets. A subset of the set of sub-models is selected for a new medical scan. A set of abnormality data is generated by applying a subset of a set of inference functions on the new medical scan, where the subset of the set of inference functions utilize the subset of the set of sub-models. Final abnormality data is generated by performing a final inference function on the set of abnormality data. The final abnormality data can be to a client device for display via a display device.

Abstract: An X-ray diagnostic apparatus according to an embodiment includes an X-ray tube, an X-ray detector, and processing circuitry. The X-ray tube exposes X-rays. The X-ray detector detects the X-rays exposed from the X-ray tube. The processing circuitry generates an X-ray image on the basis of a first detection result that is a detection result of the X-rays exposed for an irradiation period. The processing circuitry corrects the X-ray image on the basis of a second detection result that is a detection result of the X-rays until a point before the end of the irradiation period.

Abstract: A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.

Abstract: A control apparatus includes a reception unit that receives a plurality of reduced images included in a radiographic image in stages from a radiographic imaging apparatus, and a display control unit that, in a case where the radiographic image meets a pre-determined standard, displays a first image generated from the reduced images on a display unit, and in a case where the radiographic image does not meet the pre-determined standard, displays on the display unit a second image generated from more reduced images than the reduced images from which the first image is generated.

Abstract: An image processing apparatus configured to extract an irradiation field from an image obtained through radiation imaging, comprises: an inference unit configured to obtain an irradiation field candidate in the image based on inference processing; a contour extracting unit configured to extract a contour of the irradiation field based on contour extraction processing performed on the irradiation field candidate; and a field extracting unit configured to extract the irradiation field based on the contour.

Abstract: A multimode biological sample inspection apparatus and method is provided. The apparatus includes illumination hardware arrangement including transmission and sensing hardware, the illumination hardware arrangement configured to inspect a biological sample using at least two modes from a fluorescence imaging mode, a reflectance imaging mode, a scattering imaging mode, and a Raman imaging mode, and processing hardware configured to operate the illumination hardware arrangement according to a protocol including inspection settings of the at least two modes. The processing hardware receives scan results from the illumination hardware arrangement and identifies attributes of the biological sample. The processing hardware is configured to employ the attributes of at least one biological sample to alter the protocol.

Abstract: An aspect of the invention includes reading a scale in image data representing an image of physical characteristics and resizing at least a portion of the image data to align with target image data representing a target image based at least in part on the scale to form resized image data representing one or more resized images. Noise reduction is applied to the resized image data to produce test image data representing one or more test images. A best fit analysis is performed on the test image data with respect to the target image data. Test image data having the best fit are stored with training image data representing classification training images indicative of one or more recognized features. An anomaly in unclassified image data representing an unclassified image is identified based at least in part on an anomaly detector as trained using the classification training images.

Abstract: The present invention relates to a diagnosis assistance system for assisting diagnosis for a plurality of diseases based on a fundus image, the diagnosis assistance system including: a fundus image obtaining unit configured to obtain a fundus image; a first processing unit configured to, for the fundus image, obtain a first result related to a first finding of a patient using a first neural network model, a second processing unit configured to, for the fundus image, obtain a second result related to a second finding of the patient using a second neural network model, a third processing unit configured to determine, on the basis of the first result and the second result, diagnostic information on the patient, and a diagnostic information output unit configured to provide the determined diagnostic information to a user.

Abstract: A calibration method for an x-ray computerized tomography system and a method of tomographic reconstruction are provided. The calibration method includes steps of measuring at least one point spread function (PSF) at each of a plurality of points, compressing each PSF, and in one or more storing operations, storing the compressed PSFs in a computer-accessible storage medium. The PSF measurements are made in a grid of calibration points in a field of view (FOV) of the system. In the measuring step, an absorber is positioned at each of the calibration points, and an x-ray projection is taken at least once at each of those absorber positions. In the method of tomographic image reconstruction, projection data from an x-ray tomographic projection system are input to an iterative image reconstruction algorithm.

Abstract: A system and method for classifying a structure or material in an image of a subject. The system comprises: a segmenter configured to form one or more segmentations of a structure or material in an image and generate from the segmentations one or more segmentation maps of the image including categorizations of pixels or voxels of the segmentation maps assigned from one or more respective predefined sets of categories; a classifier that implements a classification machine learning model configured to generate, based on the segmentations maps, one or more classifications and to assign to the classifications respective scores indicative of a likelihood that the structure or material, or the subject, falls into the respective classifications; and an output for outputting a result indicative of the classifications and scores.

Abstract: A first image including a first sensor placed on a target can be recorded at a first resolution. Using an accelerometer, an orientation of the first image can be determined concurrently with recording of the first image. The orientation of the first image can be associated with an orientation of the first sensor placed on the target within the first image. A user input indicating a boundary surrounding the first sensor in a portion of the first image can be received via a graphical user interface (GUI). The orientation of the first sensor within the first image can be compared with a predetermined reference orientation and a determination made whether the orientation of the first sensor within the first image matches the predetermined reference orientation. When the orientation of the first sensor within the first image matches the predetermined reference orientation, a confirmation can be output via the GUI.

Abstract: A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

Abstract: The present disclosure relates to a system, method, and computer program product for computer-aided detection (CAD) of suspicious lesions in digital breast specimen radiographs, wherein single-view feature vectors from a first digital radiograph of the breast specimen are processed in a classification algorithm along with information computed from a plurality of related digital specimen radiographs to assign an overall probability of suspiciousness to potentially suspicious lesions in the first digital breast specimen radiography. In one embodiment, a greater probability of suspiciousness is determined where there are similar corresponding lesions in the first digital breast specimen radiography and in an alternate digital breast specimen radiography view of the same breast specimen.

Abstract: A detector and inclusion location method that uses a reconstruction technique to target and localize sparse small-sized but high-contrast objects, such as a tumor inside tissue. The reconstruction technique applied, can dramatically enhance the property contrast of the tumors or abnormal inclusions by ten to one hundred fold. The reconstruction technique enables the use of nonlinear imaging ill-posed techniques that are function-oriented imaging techniques without any need for structural prior knowledge.

Abstract: Disclosed is a way to provide a medical image processing device that may include a hardware processor that calculates at least one of trabecular connectivity, trabecular width, trabecular number, mineralization degree, osteoid volume, cortical width, and cortical porosity as a bone characteristic indicator of a subject from reconstructed image data generated from moiré image data acquired by photographing the subject.

Abstract: A technology for reconstructing an image of a three-dimensional object. In one example, a projection image dataset can be obtained from an imaging data detector and a reduced image dataset that has a lower quantity of data as compared to a quantity of data of the projection image dataset can be generated from the projection image dataset. An image remainder dataset can be generated to indicate an image difference between the projection image dataset and the reduced image dataset. A first scale image reconstruction of the three-dimensional object can be generated using the reconstruction technique and the reduced image dataset, and a second scale image reconstruction of the three-dimensional object can be generated using the iterative reconstruction technique and the image remainder dataset.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can collect high-quality correct answer data used for machine learning with a simple method. The image processing apparatus includes: a first extractor that extracts a measurement target region from a medical image, using a result of learning performed using correct answer data of the measurement target region; a measurement object determination unit that determines a measurement object used to measure the measurement target region; a measurement object correction unit that corrects the measurement object in response to a command from a user; and a measurement target region correction unit that corrects the measurement target region extracted by the first extractor, using a correction result of the measurement object. The first extractor performs learning using the measurement target region corrected by the measurement target region correction unit as correct answer data.

Abstract: A dynamic image processing apparatus includes a hardware processor. The hardware processor extracts (i) a region of interest and/or (ii) a frame image of interest from a series of frame images obtained by dynamic imaging of a subject. Further, the hardware processor stores, of the series of the frame images, only (i) the extracted region of interest, (ii) the extracted frame image of interest or (iii) the extracted region of interest in the extracted frame image of interest in a storage.

Abstract: A composite image (300) is created from a plurality of tomographic slices by creating a plurality of two-dimensional slice images by projecting the slices, dividing each slice image into tiles (302) according to a pattern (304), calculating a focus value for each tile (302) of each slice image, selecting one tile (302) for each position in the pattern having a highest focus measure value, and assembling the composite image (300) from said selected tiles (302).

Abstract: A data restoring method using compressed sensing and computer program product, the method includes (a) continuously measuring data for plural times to generate measurement results correspondingly, and processing the i-th measurement result using the compressed sensing, and starting to generate data matrix when times of measuring reaches a preset times of measurements N; (b) generating a first data matrix using the [(j+1)?N]th to the j—the measurements, and then generating a first restored data; (c) generating a second data matrix using the [(j+2)?N]th to the (j+1)—the measurements, and then generating a second restored data; (d) calculating an error between the first and the second restored data; (e) determining whether the error keeps constant for a predetermined number of times; (f) if not, repeating steps (c) to (e).

Abstract: A control part 30 includes: a DRR image creation part 31 that creates a DRR image including a specific site; a specific site projection part 32 that creates a projection region image representing the region of the specific site; a discriminator learning part 33 that learns a discriminator for recognizing the region of the specific site by performing machine learning with use of the DRR image and the projection region image as a training label image; a specific site region detection part 34 that detects the region of the specific site by performing discrimination using the discriminator learned by the discriminator learning part 33 on an X-ray fluoroscopic image including the specific site; and a radiation signal generation part 35 that transmits a treatment beam radiation signal to an irradiation device 90 when the region of the specific site detected by the specific site region detection part 34 is included in the radiation region of a treatment beam.

Abstract: The present invention provides a display method for a medical image, comprising: acquiring a medical image of an imaging object by means of scanning; obtaining, from the medical image, a list containing one or more objects of interest, and object information including positions of the objects of interest; acquiring, according to the positions of the objects of interest and at different imaging positions, a plurality of images that cover the objects of interest; and synthesizing the plurality of images that cover the objects of interest into a dynamic graph and dynamically displaying the dynamic graph.

Abstract: A medical image processing device having a processor configured to: acquire a medical image including an image of a subject; perform a first recognition of the medical image using a first recognizer; determine a confidence level for a recognition result of a first recognition by the first recognition; and perform a second recognition of the medical image using a second recognizer according to the confidence level for the recognition result of the first recognition, the second recognition having higher recognition accuracy than the first recognition.

Abstract: A medical scan interface feature evaluator system is operable to generate an ordered image-to-prompt mapping by selecting a set of user interface features to be displayed with each of an ordered set of medical scans. The set of medical scans and the ordered image-to-prompt mapping are transmitted to a set of client devices. A set of responses are generated by each client device in response to sequentially displaying each of the set of medical scans in conjunction with a mapped user interface feature indicated in the ordered image-to-prompt mapping via a user interface. Response score data is generated by comparing each response to truth annotation data of the corresponding medical scan. Interface feature score data corresponding to each user interface feature is generated based on aggregating the response score data, and is used to generate a ranking of the set of user interface features.

Abstract: Disclosed is a device and a method for medical image reconstruction. The method comprises obtaining, image data of a medical scanner; obtaining a noise model for the image data from the medical scanner; obtaining an initial model indicative of expected image data properties; obtaining a mapping, wherein the mapping is indicative of a mapping from the medical scanner; determining a set of candidate images based on the image data, the noise model, the initial model, and the mapping; and determining and outputting a first representation of the image data based on the set of candidate images.

Abstract: An illumination unit emits an illumination light to a specimen. An image sensor includes multiple pixels arranged in a two-dimensional manner. The image sensor captures an image of the intensity distribution of an interference pattern formed due to the illumination light that has interacted with the specimen, and outputs image data. A defect information acquisition unit acquires defect position information that indicates the positions of defective pixels of the image sensor. A processing unit reconstructs a subject image that represents the specimen based on the image data and the defect position information.