Abstract: A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.

Abstract: Aspects of the disclosure relate to using machine learning for foreground mask estimation. A computing platform may receive a set of images and corresponding ground truth foreground masks. Using the set of images and the corresponding ground truth foreground masks, the computing platform may train the first neural network to distinguish between image foregrounds and backgrounds, which may result in a first set of foreground masks. For each image and based on a corresponding foreground mask, the computing platform may estimate a first background clean plate. Using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, the computing platform may train a second neural network, which may configure the second neural network to output foreground masks based on video input information. The computing platform may deploy, to an implementation computing device, the second neural network.

Abstract: Aspects of the disclosure relate to using machine learning for foreground mask estimation. A computing platform may receive a set of images and corresponding ground truth foreground masks. Using the set of images and the corresponding ground truth foreground masks, the computing platform may train the first neural network to distinguish between image foregrounds and backgrounds, which may result in a first set of foreground masks. For each image and based on a corresponding foreground mask, the computing platform may estimate a first background clean plate. Using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, the computing platform may train a second neural network, which may configure the second neural network to output foreground masks based on video input information. The computing platform may deploy, to an implementation computing device, the second neural network.

Abstract: A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components.

Abstract: A video background processing system is disclosed that is configured to receive a video stream including a plurality of successive first video frames at a first resolution. The system comprises a video resolution modifier configured to reduce the resolution of the first video frames from the first resolution to a second resolution lower than the first resolution and thereby generate second video frames. The system also comprises a foreground determiner configured to determine a foreground portion and a background portion in the second video frames and to produce first foreground data indicative of locations of the foreground and background portions in the second video frames at the second resolution, wherein the foreground determiner is configured to use the first foreground data to generate second foreground data indicative of locations of the foreground and background portions in the first video frames.

Abstract: A system for enhancing a depth map associated with a 2D image wherein a face detector analyzes the 2D image to determine a position of a face in the 2D image, the system utilizes the position to derive a depth model; and the depth model is combined with the depth map to generate an enhanced depth map.

Abstract: A stereoscopic rendering system is disclosed that comprises a depth buffer having at least two depth buffer portions respectively corresponding to different views of a scene, the depth buffer arranged to store depth values indicative of the depth of pixels in a scene, and the depth buffer portions having different associated depth value ranges so that the different depth buffer portions are distinguishable from each other. The system also includes an image buffer arranged to store information indicative of an image to be displayed. The system is arranged to apply a different depth test for each view of the scene such that only pixels of the view that spatially correspond to the depth buffer portion associated with the view are rendered to the image buffer. The image rendered into the image buffer comprises image portions respectively spatially corresponding to the different depth buffer portions and the different views of the scene.

Abstract: A system for enhancing a depth map associated with a 2D image wherein a face detector analyzes the 2D image to determine a position of a face in the 2D image, the system utilizes the position to derive a depth model; and the depth model is combined with the depth map to generate an enhanced depth map.

Abstract: A method of creating at least one depth map for an image sequence including the steps of receiving depth data for a plurality of points in the image sequence, utilising the depth data and a classifier to ascertain depth characteristics as a function of image characteristics and relative position and creating a depth map for at least one frame of the image sequence utilising the image characteristics.

Abstract: A method of creating a depth map including the steps of assigning a depth to at least one pixel or portion of an image, determining relative location and image characteristics for each at least one pixel or portion of the image, utilizing the depth(s), image characteristics and respective location to determine an algorithm to ascertain depth characteristics as a function relative location and image characteristics, utilizing said algorithm to calculate a depth characteristic for each pixel or portion of the image, wherein the depth characteristics form a depth map for the image. In a second phase of processing the said depth maps form key frames for the generation of depth maps for non-key frames using relative location, image characteristics and distance to key frame(s).

Abstract: A method of generating images suitable for use with a multi-view stereoscopic display including the steps of intercepting data passed from an application to an application programming interface, the data representing a scene or object to be displayed on the display; processing the data to render multiple views of the scene or object; creating modified data by modifying the intercepted data to represent the multiple views; and passing the modified data to the application programming interface.

Abstract: A method of creating at least one depth map for an image sequence including the steps of receiving depth data for a plurality of points in the image sequence, utilising the depth data and a classifier to ascertain depth characteristics as a function of image characteristics and relative position and creating a depth map for at least one frame of the image sequence utilising the image characteristics.

Abstract: A method of creating a depth map including the steps of assigning a depth to at least one pixel or portion of an image, determining relative location and image characteristics for each at least one pixel or portion of the image, utilising the depth(s), image characteristics and respective location to determine an algorithm to ascertain depth characteristics as a function relative location and image characteristics, utilising said algorithm to calculate a depth characteristic for each pixel or portion of the image, wherein the depth characteristics form a depth map for the image. In a second phase of processing the said depth maps form key frames for the generation of depth maps for non-key frames using relative location, image characteristics and distance to key frame(s).