An oral cavity scanning device is provided in the invention. The oral cavity scanning device includes an image capturing unit, an IMU circuit and a processing unit. The image capturing unit obtains a first image and a second image. The IMU circuit obtains IMU information corresponding to the first image and the second image. The processing unit obtains a distance value between the first image and the second image. The processing unit uses a contour algorithm to obtain a first contour and a second contour. The processing unit obtains first sampling points according to the first contour and second sampling points according to the second contour. The processing unit uses a feature algorithm to find relative feature points between the first sampling points and the second sampling points. The processing unit uses a depth information algorithm to obtain the depth information of each feature point.

An oral cavity scanning device is provided in the invention. The oral cavity scanning device includes an image capturing unit, an IMU circuit and a processing unit. The image capturing unit obtains a first image and a second image. The IMU circuit obtains IMU information corresponding to the first image and the second image. The processing unit obtains a distance value between the first image and the second image. The processing unit uses a contour algorithm to obtain a first contour and a second contour. The processing unit obtains first sampling points according to the first contour and second sampling points according to the second contour. The processing unit uses a feature algorithm to find relative feature points between the first sampling points and the second sampling points. The processing unit uses a depth information algorithm to obtain the depth information of each feature point.

A system for patient cardiac imaging and tissue modeling. The system includes a patient imaging device that can acquire patient cardiac imaging data. A processor is configured to receive the cardiac imaging data. A user interface and display allow a user to interact with the cardiac imaging data. The processor includes fat identification software conducting operations to interact with a trained learning network to identify fat tissue in the cardiac imaging data and to map fat tissue onto a three-dimensional model of the heart. A preferred system uses an ultrasound imaging device as the patient imaging device. Another preferred system uses an MRI or CT image device as the patient imaging device.

The present embodiments relate generally to systems and methods for performing a measurement on an ultrasound image displayed on a touchscreen device. The method may include: receiving, via the touchscreen device, first input coordinates corresponding to a point on the ultrasound image; using the first input coordinates as a seed for performing a contour identification process on the ultrasound image, wherein the contour identification process performs contour evolution using morphological operators to iteratively dilate from the first input coordinates; upon identification of a contour from the contour identification process, placing measurement calipers on the identified contour; and storing a value identified by the measurement calipers as the measurement.

The present embodiments relate generally to systems and methods for performing a measurement on an ultrasound image displayed on a touchscreen device. The method may include: receiving, via the touchscreen device, first input coordinates corresponding to a point on the ultrasound image; using the first input coordinates as a seed for performing a contour identification process on the ultrasound image, wherein the contour identification process performs contour evolution using morphological operators to iteratively dilate from the first input coordinates; upon identification of a contour from the contour identification process, placing measurement calipers on the identified contour; and storing a value identified by the measurement calipers as the measurement.

A method for translating an image, a method for training an image translation model, and related electronic devices are proposed. In the method for translating an image, an image translation request carrying an original image is obtained. A down-sampled image is generated by down sampling the original image. A pre-translated image, a mask image, and deformation parameters are generated based on the down-sampled image. A size of the pre-translated image and a size of the mask image are the same as a size of the original image. A deformed image is obtained by deforming original image based on the deformation parameters. The deformed image, the pre-translated image and the mask image are fused to generate a target translation image.

A method for translating an image, a method for training an image translation model, and related electronic devices are proposed. In the method for translating an image, an image translation request carrying an original image is obtained. A down-sampled image is generated by down sampling the original image. A pre-translated image, a mask image, and deformation parameters are generated based on the down-sampled image. A size of the pre-translated image and a size of the mask image are the same as a size of the original image. A deformed image is obtained by deforming original image based on the deformation parameters. The deformed image, the pre-translated image and the mask image are fused to generate a target translation image.

The disclosure provides methods and systems for automatically generating an animatable object, such as a 3D model. In particular, the present technology provides fast, easy, and automatic animatable solutions based on unique facial characteristics of user input. Various embodiments of the present technology include receiving user input, such as a two-dimensional image or three-dimensional scan of a user's face, and automatically detecting one or more features. The methods and systems may further include deforming a template geometry and a template control structure based on the one or more detected features to automatically generate a custom geometry and custom control structure, respectively. A texture of the received user input may also be transferred to the custom geometry. The animatable object therefore includes the custom geometry, the transferred texture, and the custom control structure, which follow a morphology of the face.

A method of segmentation of a medical image of a human brain including obtaining a voxelized 3D medical image of the human brain, computing at least two surface models of the human brain using a BET method, each surface model being computed for a unique fractional constant b.sub.t. For each computed surface model, determining a volume included in the computed surface model, thereby obtaining a set of sample pairs, fitting a curve to the sample pairs of the set, determining an inflection point of the curve, identifying a fractional constant bt corresponding to the determined inflection point, and computing the surface model of the human brain using the BET method that is dependent of bt.

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.