Disclosed is a three-dimensional ultrasound image display method comprising the following steps: S1: obtaining a series of original two-dimensional images having spatial position and angle information by means of automatic or manual scanning; S2: performing image reconstruction on the basis of the original two-dimensional images to obtain three-dimensional volumetric images; S3: obtaining, from the three-dimensional volumetric images, one or more section images intersecting the original two-dimensional images, and obtaining one or more reconstructed two-dimensional images by means of image processing; S4: displaying together the one or more original two-dimensional images and the one or more section images in a three-dimensional space; and S5: selecting and displaying feature points, feature lines, and feature surfaces in the three-dimensional space on the basis of the original two-dimensional volumetric images. The present method provides an efficient and high-precision three-dimensional image display method, which can be widely applied to ultrasound and other three-dimensional imaging modes.

Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.

The present disclosure involves a reference line determination method and system. In a process of determining a reference line, a plurality of original images containing a first spatial position information are obtained. According to the plurality of original images, a composite image containing a second spatial position information is further determined. After a composition relationship between a plurality of original images was determined, a reference line is determined on the composite image according to the spatial position information.

A method is described for adapting 3D image datasets so that they can be registered and combined with 2D images of the same subject, wherein deformation or movement of parts of the subject has occurred between obtaining the 3D image and the 2D image. 2D-3D registrations of the images with respect to multiple features visible in both images are used to provide point correspondences between the images in order to provide an interpolation function that can be used to determine the position of a feature visible in the first image but not the second image and thus mark the location of the feature on the second image. Also described is apparatus for carrying out this method.

Systems, devices, and methods for identifying a region of interest are provided. A plurality of skeletal landmarks may be identified from an image received from an imaging device. A pose of a patient may be determined based on the plurality of skeletal landmarks. A region of interest may be identified on the patient based on the determined pose. Instructions may be automatically provided to the controller to adjust a pose of a surgical instrument relative to the region of interest. The plurality of skeletal landmarks may be tracked for movement. The region of interest may be updated when movement of the plurality of skeletal landmarks is detected.

A radiographic image display apparatus 3 included in a radiographic imaging system 100 includes a hardware processor that obtains an image data item on each of the frame images generated by the radiographic imaging apparatus 2, detects a situation of the subject at least at a time point in the dynamic imaging, associates the detected situation of the subject with the obtained image data items, and issues a specific output for notification that the subject is in a specific situation when the subject is in a state of a specific frame image fs, in a case where the display is caused to display the specific frame image fs, this specific frame image fs being taken when it is detected that the situation of the subject is the specific situation.

Systems and methods for performing surgery based on an analysis of images captured in at least two different planes are disclosed. According to some embodiments, a first X-ray image of a spine in a first plane and a second X-ray image of the spine in a second plane are obtained, and a curve is drawn on the first and second X-ray images so that the curve tracks the vertebral bodies of the spine. The coordinates of the curve in the first and second X-ray images are determined by performing image processing to detect the curve in the X-ray images. A three-dimensional model of the spine is constructed based on the coordinates. The model is analyzed based on medical data relating to the spine and models of other spines to determine parameters of a spinal device. The spinal device is constructed and deployed in the spine based on the parameters.

The present disclosure provides a system and method for image data acquisition. The method may include obtaining image data of a subject including a first type of tissue and a second type of tissue. The method may include determining, based on the image data of the subject, a target portion including at least a portion of at least one of the first type of tissue or the second type of tissue. The method may include determining, based at least in part on the target portion represented in the image data, a scan mode corresponding to the target portion. The method may include causing an imaging device to acquire, based on the scan mode, image data of the target portion.

Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging fracture model are presented. In one example, a system employs a first convolutional neural network associated with vertebrae segmentation to generate learned vertebrae segmentation data regarding a spine anatomical region related to a CT image. The system also employs a second convolutional neural network associated with fracture segmentation to generate, based on the learned vertebrae segmentation data, learned fracture segmentation data regarding the spine anatomical region. Furthermore, the system detects presence or absence of a medical fracture condition in the CT image based on the learned vertebrae segmentation data and the learned fracture segmentation data.

Systems and techniques for determining degree of motion using machine learning to improve medical image quality are presented. In one example, a system generates, based on a convolutional neural network, motion probability data indicative of a probability distribution of a degree of motion for medical imaging data generated by a medical imaging device. The system also determines motion score data for the medical imaging data based on the motion probability data.