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, apparatus, device, and storage medium for transforming a hairstyle are provided. The method may include: determining a face bounding box according to information on face key points of acquired face image; constructing grids according to the face bounding box; deforming, by using an acquired target hairstyle function, edge lines of at least a part of the constructed grids, which comprises the hairstyle, to obtain a deformed grid curve; determining a deformed hairstyle in the face image according to the deformed grid curve.

An ultrasound imaging system is for determining stroke volume and/or cardiac output. The imaging system may include a transducer unit for acquiring ultrasound data of a heart of a subject (or an input for receiving the acquired ultrasound data), and a controller. The controller is adapted to implement a two-step procedure, the first step being an initial assessment step, and the second being an imaging step having two possible modes depending upon the outcome of the assessment. In the initial assessment procedure, it is determined whether regurgitant ventricular flow is present. This is performed using Doppler processing techniques applied to an initial ultrasound data set. If regurgitant flow does not exist, stroke volume is determined using segmentation of 3D ultrasound image data to identify and measure the volume of the left or right ventricle at each of end systole and end-diastole, the difference between them giving a measure of stroke volume. If regurgitant flow does exist, stroke volume is determined using Doppler techniques applied to ultrasound data continuously collected throughout a cardiac cycle.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.

The disclosure relates to a method and to an image processing facility configured to carry out the method for the segmentation of image data of a target object. In the method, a first segmentation is generated by a trained algorithm. Furthermore, a statistical shape and appearance model is provided, which is trained on corresponding target objects. An interference region is further determined, in which the image data is impaired by an image artifact. A final segmentation of the image data is then generated by adjusting the shape and appearance model to the respective target object outside the interference region and using the first segmentation in the interference region.

Supported by artificial intelligence, an object is classified in an X-ray projection image. A 3D representation as well as a localization of the classified object can be determined by matching a model of the classified object to a visualization of the classified object in the X-ray image.

An occupant modeling device includes: an acquisition section acquiring an image by imaging a region where there is a probability that a face of an occupant is present; a model fitting section generating a model of the face based on a first image acquired by the acquisition section; a tracking section adapting the model to a second image acquired after the first image; a determination section determining correctness of a facial part position included in the second image to which the model is adapted, by using learned information obtained through learning based on correct information and incorrect information regarding the facial part position; and a processing section determining whether a process in the tracking section is to be continuously executed or a process in the model fitting section is to be executed again according to a determination result in the determination section.

The present disclosure discloses a method and a system for identifying goods of intelligent shopping cart. The method comprises: reading bar code information of a to-be-purchased goods and obtaining corresponding prestored goods information; continuously detecting and obtaining a total goods weight m.sub.n+1 in the shopping cart, and comparing the total goods weight m.sub.n+1 with a total goods weight m.sub.n acquired after a previous purchasing action is completed, to obtain a variation m.sub.Δ of the total goods weight. According to the method of the present disclosure, when a customer puts a goods in the shopping cart in the shopping course, the correct goods is automatically identified and recorded in a shopping list, then the customer can directly settle the account after completing the shopping, accordingly a lot of time for the customers to wait for the settlement is saved.

Automated and objective methods for quantifying a retinal characteristic include segmenting an optical coherence tomography retinal image into a plurality of layered retinal regions, and quantifying the retinal characteristic for each region as normalized to a range defined by the characteristic value in the vitreous region and in the retinal pigment epithelium region. Such methods are useful for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.