A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

A localized evaluation network incorporates a discriminator acting as classifier, which may be included within a generative adversarial network (GAN). GAN may include a generative network such as U-NET for creating segmentations. The localized evaluation network is trained on image pairs including medical images of organs of interest and segmentation (mask) images. The network is trained to distinguish whether an image pair does or does not represent the ground truth. GAN examines interior layers of the discriminator and evaluates how much each localized image region contributes to the final classification. The discriminator may analyze regions of the image pair that contribute to a classification by analyzing layer weights of the machine learning model. Disclosed embodiments include a visual attribute, such as a heat map, that represents contributions of localized regions of a contour to an overall confidence score. These localized regions may be highlighted and reported for quality assurance review.

The invention relates to a method for determining errors in at least one parameter of the object derived from a digital representation of an object, wherein the digital representation comprises a large number of pixels arranged on a grid. At least one item of image information that quantifies a material-specific value of the object at the position of the pixel is assigned to a pixel. The image information results from a metrological mapping of the object, and is overlaid with statistical noise. As a result of the metrological mapping of the object, the image information of a first pixel is correlated to the image information of pixels within a surroundings of the first pixel defined by a correlation length of the image.

A surgical system can be configured to obtain image data of a joint that comprises at least a portion of a humerus; segment the image data to determine a shape for a diaphysis of the humerus; based on the determined shape of the diaphysis, determine an estimated pre-morbid shape of the humerus; based on the estimated shape of the humerus, identify one or more bone fragments in the image data; and based on the identified bone fragments in the image data, generate an output.

An X-ray imaging apparatus includes an X-ray generator including a plurality of X-ray sources, an X-ray detector configured to detect X-rays radiated from the plurality of X-ray sources and generate a plurality of pieces of projection data, and a processor configured to apply log projection to each of the plurality of pieces of projection data, to apply weighted projection to the log-projected projection data, to apply a bidirectional ramp filter to the weighted-projected projection data, and to generate a tomographic image reconstructed based on each of the projection data to which the bidirectional ramp filter is applied.

A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a type of the structure of interest in a region in which the structure of interest has been detected and generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.

Techniques of determining a quantification of at least one characteristic of a muscle structure comprising at least one muscle and at least one tendon are disclosed. The quantification of the at least one characteristic of the rotator cuff may be determined by using at least one artificial neural network and based on one or more medical images depicting the muscle structure of a patient.

The present disclosure relates to a method for three-dimensionally modeling an organ through image segmentation. The three-dimensional modeling of an organ includes the operations of: receiving one or more pieces of medical image data for a specific bodily organ of a target object; setting a region of interest with respect to the bodily organ based on the one or more pieces of medical image data; forming one or more blocks corresponding to the region of interest, wherein the blocks include a portion of the bodily organ corresponding to the regions of interest; setting a segment algorithm for each of the blocks; generating first image data respectively performing 3D modeling of portions contained in the blocks based on algorithms set to the blocks; and merging the first image data, and generating a three-dimensional section image data with respect to the entire bodily organ.

An information processing apparatus includes at least one processor, and the processor derives a property for at least one predetermined property item which is related to a structure of interest included in an image. The processor specifies a basis region serving as a basis for deriving the property related to the structure of interest for each property item and derives a basis image including the basis region.