A computer-implemented method includes receiving a 3D model representative of upper teeth (U1) of a patient (P) and receiving a plurality of images of a face of the patient (P). The method also includes generating a facial model (200) of the patient based on the received plurality of images and determining, based on the determined facial model (200), the received 3D model of 10 the upper teeth (U1) and the plurality of images, a spatial relationship between the upper teeth (U1) of the patient (P) and a facial skeleton of the patient (P).

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

A method is for providing a medical image of a patient, acquired via a computed tomography apparatus. An embodiment of the method includes acquiring first projection data of a first measurement region; acquiring second projection data of a second measurement region; registering a reference image to the at least one respiration-correlated image of the patient, wherein the reference image corresponds to the at least one functional image of the patient or is reconstructed under a second reconstruction rule from the second projection data, to produce a deformation model; applying the deformation model to the at least one functional image of the patient; combining the at least one functional image of the patient, deformed by the applying of the deformation model, with the at least one respiration-correlated image of the patient, to produce the medical image of the patient; and providing the medical image of the patient.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

There is provided a method for generating a personalized Head Related Transfer Function (HRTF). The method can include capturing an image of an ear using a portable device, auto-scaling the captured image to determine physical geometries of the ear and obtaining a personalized HRTF based on the determined physical geometries of the ear.

Presented are concepts for detecting subject motion in medical imaging of a subject. One such concept obtains a motion classification model representing relationships between motion of image features and subject motion values. For each of a plurality of medical slice images of an imaged volume of the subject, an image feature of the medical slice image is extracted. Based on the extracted image feature for each of the plurality of medical slice images, motion information for the image feature is determined. Based on the motion information for the image feature and the obtained motion classification model, a subject motion value is determined.

Methods and systems are disclosed for performing operations for applying augmented reality elements to a fashion item. The operations include receiving an image that includes a depiction of a person wearing a fashion item. The operations include generating a segmentation of the fashion item worn by the person depicted in the image. The operations include extracting a portion of the image corresponding to the segmentation of the fashion item; estimating an angle of each pixel in the portion of the image relative to a camera used to capture the image. The operations include applying one or more augmented reality elements to the fashion item in the image based on the estimated angle of each pixel in the portion of the image relative to the camera used to capture the 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.

Methods and systems are disclosed for performing operations for applying augmented reality elements to a fashion item. The operations include receiving an image that includes a depiction of a person wearing a fashion item. The operations include generating a segmentation of the fashion item worn by the person depicted in the image. The operations include extracting a portion of the image corresponding to the segmentation of the fashion item; estimating an angle of each pixel in the portion of the image relative to a camera used to capture the image. The operations include applying one or more augmented reality elements to the fashion item in the image based on the estimated angle of each pixel in the portion of the image relative to the camera used to capture the image.