Systems and methods are provided for processing X-ray images, wherein the methods are implemented as a software program product executable on a processing unit of the systems. Generally, an X-ray image is received by the system, the X-ray image being a projection image of a first object and a second object. The first and second objects are classified and a respective 3D model of the objects is received. At the first object, a geometrical aspect like an axis or a line is determined, and at the second object, another geometrical aspect like a point is determined. Finally, a spatial relation between the first object and the second object is determined based on a 3D model of the first object, a 3D model of the second object, and the information that the point of the second object is located on the geometrical aspect of the first object.

A technique of generating surgical information from intra-operatively acquired image data of vertebrae and pre-operatively acquired image data of the vertebrae is presented. A method implementation includes obtaining first image segments each containing a different vertebra, and second image segments each containing a different vertebra. The first image segments have been derived by processing the pre-operatively acquired image data, and the second image segments have been derived by processing the inter-operatively acquired image data. The method includes identifying one of the second image segments and one of the first image segments that contain the same vertebra, and determining a transformation that registers the identified first image segment and the identified second image segment. The method includes generating surgical information based on the transformation and the identified first image segment.

Disclosed herein are systems, methods, and media for updating preoperative 3D dataset using at least two 2D intraoperative images to reflect changes in anatomical features caused by patient movement.

Disclosed are a bone age estimation method and a bone age estimation apparatus. The bone age estimation method may comprise the steps of: extracting a region of interest including a cervical spine region from a lateral cephalometric radiographic image obtained by imaging a subject's cervical spine, by using a first deep learning model; extracting landmarks from the extracted region of interest by using a second deep learning model; calculating a landmark numerical value on the basis of the extracted landmarks; and providing maturity information of a maturation stage of the cervical spine on the basis of the calculated landmark numerical value.

A system according to at least one embodiment of the present disclosure includes an imaging source; an imaging detector; a depth sensor; and a controller, where the controller receives image information from the depth sensor, determines a gesture in relation to a working volume, and moves the imaging source and the imaging detector relative to the working volume based on the gesture.

A method of quantifying a size of a tissue of interest in a animal through a device including a storage, an image processor, and a display by using an X-ray image of the animal is proposed. The proposed method may include storing the X-ray image in the storage, displaying the X-ray image on the display, performing, by the image processor, processes of (i) calculating a reference value for a length of a reference tissue of the animal displayed on the X-ray image, (ii) calculating a value of a length in at least one specific direction of the tissue of interest of the animal displayed on the X-ray image, and (iii) quantifying the size of the tissue of interest as a ratio of the value of the length to the reference value, and displaying the quantified size of the tissue of interest on the display.

A computer-implemented method of detecting and quantifying a spinal curve is disclosed herein. The method comprises obtaining an infrared radiometer camera, positioning the infrared radiometer camera for receiving thermal data for a spine of a subject, the camera being horizontally spaced about ½ meters to about 3 meters from the spine, scanning at least a portion of the spine with the infrared radiometer camera to obtain the thermal data, analyzing the thermal data using machine learning software which uses a classification algorithm to determine the presence of the spinal curve, and calculating a first Cobb angle for the curve of the subject's spine. Corresponding systems and additional methods also are disclosed.

A plurality of image projections are acquired at faster than cardiac rate. A spatiotemporal reconstruction of cardiac frequency angiographic phenomena in three spatial dimensions is generated from two dimensional image projections using physiological coherence at cardiac frequency. Complex valued methods may be used to operate on the plurality of image projections to reconstruct a higher dimensional spatiotemporal object. From a plurality of two spatial dimensional angiographic projections, a 3D spatial reconstruction of moving pulse waves and other cardiac frequency angiographic phenomena is obtained. Reconstruction techniques for angiographic data obtained from biplane angiography devices are also provided herein.

Disclosed herein are systems and methods for vertebral bone segmentation and vertebral bone enhancement in medical images.

Disclosed are a method and an apparatus of processing image. The method includes: obtaining an initial bone segmentation result; and fusing the initial bone segmentation result based on characteristics of and correspondences between a plurality of bone segmentation results in the initial bone segmentation result, to obtain a target bone segmentation result. The initial bone segmentation result includes the plurality of bone segmentation results generated by a plurality of different deep learning models. Methods in the embodiments of the present application can improve precision of a fusion result of the plurality of bone segmentation results.