A method for automatically determining spine deformation from an image showing a number of vertebrae of the spine, comprises: detecting center points of the number of vertebrae shown in the image; constructing a center line based on the center points; computing a local tilt at points along the center line; determining positive and negative tilt-maxima of the local tilt and selecting two reference vertebrae having center points closest to the positive and negative tilt-maxima; segmenting an upper endplate of the cranial reference vertebra; segmenting a lower endplate of the caudal reference vertebra; computing an angle between the upper endplate and the lower endplate; and outputting the angle.

A bundle includes five or more circular elongated elements. Each circular elongated element includes: a first end; a cylindrical portion defining an outer diameter of the circular elongated element and a rotation axis of the circular elongated element; and a second end. The first end and/or the second end of each circular elongated element fulfills at least one of the following equations: ARO≤A or ARO/OD≤B, where value A is 1.3 mm, ARO is an axial run out in mm of the first end and/or the second end of the respective circular elongated element, value B in mm/mm is 0.1, and OD is the outer diameter in mm of the respective circular elongated element.

The present disclosure relates to systems and methods for object recognition. The systems may obtain image data captured by an imaging device. The image data may include one or more objects. The systems may determine a centerline of a target object in the one or more objects based on the image data. The systems may determine a recognition result of the target object using a trained neural network model based on at least one feature parameter of the centerline of the target object. The recognition result may include a name of the target object. The systems may perform an anomaly detection on the target object based on the recognition result of the target object.

A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

In one embodiment, a medical image processing apparatus includes: processing circuitry configured to extract 3D blood vessel data of an object from 3D image data of the object, detect a tip position of a medical device moving in a blood vessel in real time from a fluoroscopic image of the object inputted during an operation, and calculate at least one of a recommended route and a recommended direction of the medical device from the 3D blood vessel data, a rough route of the medical device, and the tip position of the medical device; and a terminal device configured to display a 3D blood vessel image of the object generated from the 3D blood vessel data and to designate the rough route of the medical device on the 3D blood vessel image.

Various embodiments relate to a computer apparatus for the bone microstructure connectivity recovery of a skeletal image reconstructed through an artificial neural network using the representations of a node-link graph-based bone microstructure and a method thereof. The computer apparatus and the method may be configured to represent a node-link graph from a bone microstructure of an input skeletal image, reinforce a connectivity of the bone microstructure in the node-link graph, and change the node-link graph into a skeletal image.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image related to the pancreas. The processing circuitry is configured to extract a pancreas region included in the medical image. The processing circuitry is configured to extract at least one tubular structure region from the inside of the pancreas region. The processing circuitry is configured to identify a first end part and a second end part related to the pancreas on the basis of the pancreas region. The processing circuitry is configured to estimate a primary pancreatic duct centerline of the pancreas on the basis of the tubular structure region, the first end part, and the second end part.

A method for skeletonizing the strands of a composite material part in a 3D image having a step of detecting oriented section centers of the strands and a link from each center to the closest center having the same orientation, the detection being carried out by means of at least one reference volume having an oriented centered strand pattern, the detection step having, for each reference volume: • a step of determining portions of the image, • a step of calculating a correlation score between the reference volume and each portion associated with the central voxel of each portion, so as to obtain a correlation score for each voxel of the image; and • a step of determining the strand centers corresponding to the voxels having a correlation score which corresponds to a local maximum.

Disclosed is a method of high-precision 3D reconstruction of existing railway track lines based on UAV multi-view images, including: acquiring initial data, acquiring a UAV image rail top centerline, calculating a rail top centerline based on a nonlinear least squares method, and calculating three-dimensional coordinates of the rail centerline. Based on the multi-view geometry principle in computer vision and photogrammetry, object space coordinates of the line can be directly calculated by using image information, which does not require outdoor workers to work online and can effectively improve the safety of railway operation line surveying and mapping. Therefore, this method has important engineering application value and application prospect.

The embodiments of the present disclosure provides methods for processing a plaque implemented on at least one machine each of which has at least one processor and at least one storage device for. The method may include: obtaining a plurality of images corresponding to a target vessel; processing the plurality of images; and determining plaque information based on a processing result. The plaque information may include at least one of an identification result of a target plaque, plaque composition distribution, or a detection result of plaque stability.