The present invention relates to an X-ray imaging system (10). The X-ray imaging system comprises an optical camera (20), an X-ray imaging device (30), a processing unit (40), and an output unit (50). The optical camera is configured to acquire an optical image of a body part (BP) of a patient or an optical image of the head of the patient. The X-ray imaging device is configured to acquire an X-ray image of the body part of the patient. The processing unit is configured to determine an orientation of the body part, the determination comprising utilization of the optical image of the body part or utilization of the optical image of the head of the patient. The processing unit is configured to annotate the X-ray image of the body part with the orientation. The output unit is configured to output the orientation annotated X-ray image.

Automated assessment for a fetus may be applied based on imaging data obtained during medical imaging examination of the fetus, with the applying including processing imaging data corresponding to a plurality of a cross-section imaging slices corresponding to a limb of the fetus, where the processing includes for each imaging slice: automatically generating a predicted outer mask for an outer contour of the limb based on application of a first pre-trained model to imaging data corresponding to the imaging slice; and automatically generating a segmentation of fat-lean mask for the imaging slice based on application of a second pre-trained model to both of the imaging data corresponding to the imaging slice and the generated predicted output mask; and applying based on the processing of the imaging data corresponding to the plurality of a cross-section imaging slices: a fractional limb volume assessment; and a fat-lean mass assessment.

Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

A method and a corresponding system are provided. The method comprises steps of providing 2D images and subsequently detecting outlines of a primary structure in each of the images. A visual representation of the 2D images is generated and the 2D images are then arranged as 2D slices in a 3D visual representation. To this end, at least two of the 2D images are taken at different imaging angles. The method provides a 3D visual representation of a region of interest comprising a primary structure to support a spatial sense of a user.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences

There is provided an image processing apparatus performing a processing of correcting a correction target pixel in a radiation image by using a radiation detector that includes a pixel region in which a plurality of pixels configured to detect a radiation are provided in a matrix, and that is configured to obtain the radiation image by alternately reading out a charge accumulated in a pixel in an odd column or a pixel in an even column in each row of the pixel region, the image processing apparatus comprising: a pixel determination unit configured to determine a reference pixel from among four pixels vertically and horizontally adjacent to the correction target pixel in the radiation image; and a pixel correcting unit configured to correct the correction target pixel in the radiation image using the reference pixel.

System, device, and method of determining Anisomelia or Leg Length Discrepancy (LLD) of a subject, by using image analysis and machine learning. A system includes a plurality of end-user devices; each device includes a camera to capture digital non-radiological non-X-Ray photographs of legs of a person; each device further includes a local Deep Neural Network (DNN) engine to perform local classification of images as either manifesting LLD or non-manifesting LLD. The digital non-radiological non-X-Ray photographs are also uploaded from the end-user devices to a central server, which updates and upgrades the DNN model based on transfer learning, and periodically distributes the upgraded DNN model downstream to the end-user devices.

Patient specific implant technology, in which an outline representation of a portion of an outer surface of a periphery of a bone volume is determined and the outline representation is used in operations related to implant matching. In addition, an instrument may be made to match a perimeter shape of a Patient Specific Knee Implant with features for locating holes in a distal femur such that posts or lugs in a femoral implant locate the femoral implant centered medial-laterally within an acceptable degree of precision to prevent overhang of either the side of the femoral implant over the perimeter of the distal femur bone resections. Further, a two-dimensional outline representation may be segmented into segments that correspond to resection cuts used in fitting an implant on a portion of a bone and operations related to implant matching may be performed based on the segments.

Described are systems, targets, and methods for registering a tool for use in optical tracking. A first target and a second target are attached to the tool, with the first target having a known spatial relationship to the tool or end effector of the tool. By determining a spatial feature of the first target and a pose of the second target, and using the known spatial relationship between the first target and the tool or end effector of the tool, a spatial relationship between the second target and the end effector can be determined. Subsequently the first target can be removed, and the end effector is trackable based on only tracking of the second target. In some implementations, the first target is removably couplable to the tool by the same interface by which the end effector is removably couplable to the tool.