Volume data is used for extracting a contour of a measurement object and measurement information describing anatomical structure useful for diagnosis is acquired from the contour. When volume data of a subject is inputted (S301), an image processing device detects feature points in the volume data (S302); detects contours of a plurality of parts in the volume data based on the detected feature points and anatomical definitions (S303); and optimizes boundary lines defining contours of parts contacting each other, out of the detected plural parts, so as to combine together the optimized contours of the plural parts for creating a contour of the measurement object (S304). Measurements are taken on diagnostic items useful for diagnosis based on the created contour (S305); and the acquired measurement information is outputted as measurement results (S306) which are displayed at a display.

This application discloses methods, apparatus, and electronic devices for face pose estimation and three-dimensional face reconstruction. The face pose estimation method comprises: acquiring a two-dimensional face image for processing, constructing a three-dimensional face model corresponding to the two-dimensional face image, and determining a face pose of the two-dimensional face image based on face feature points of the three-dimensional face model and face feature points of the two-dimensional face image. With this approach, the face pose estimation is performed based on the three-dimensional face model corresponding to the two-dimensional face image, instead of only based on a three-dimensional average face model. As a result, a high accuracy pose estimation can be obtained even for a face with large-angle and exaggerated facial expressions. Thus, robustness of the pose estimation can be effectively improved.

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.

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

The present invention relates to a medical image processing device (10), comprising: —a receiving unit (60) for receiving a first and a second medical image (72, 74) of an anatomical object of interest (84), wherein each of the first and the second medical images (72, 74) comprises a different field of view of the anatomical object of interest (84), and wherein the first medical image and the second medical image (72, 74) show a same or similar anatomical state of the anatomical object of interest (84); —a registration unit (64) that is configured to determine a transformation from an image space of the second medical image (74) to an image space of the first medical image (72); —a transformation unit (66) that is configured to transform the second medical image (74) into the image space of the first medical image (72) based on said transformation in order to receive a transformed second medical image (74′); and —a segmentation unit (68) that is configured to perform an overall segmentation that makes use of both the first medical image (72) and the transformed second medical image (74′) without fusing the first medical image (72) and the transformed second medical image (74′), wherein one and the same segmentation model (92) is simultaneously adapted to both the first medical image (72) and the transformed second medical image (74′) by identifying a first set of feature points (80) of the anatomical object of interest (84) within the first medical image (72), by identifying a second set of feature points (82) of the anatomical object of interest (84) within the transformed second medical image (74′), and by adapting the segmentation model (92) to both the first and the second set of feature points (80, 82).

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.

A surgical instrument navigation system is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.

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.

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.

In a method for generating a surrogate marker based on medical image data mapping an image region, the medical image data is detected using a first interface, a first subregion of the image region is selected by segmenting a first structure included in the image region, a first property of the first subregion is extracted, the surrogate marker is determined based on the first property, and the surrogate marker is provided using a second interface.