A marker-coordinate detecting unit detects coordinates of a stent marker on a new image when the new image is stored in an image-data storage unit; and then a correction-image creating unit creates a correction image from the new image through, for example, image transformation processing, so as to match up the detected coordinates with reference coordinates that are coordinates of the stent marker already detected by the marker-coordinate detecting unit in a first frame. An image post-processing unit then creates an image for display by performing post-processing on the correction image created by the correction-image creating unit, the post-processing including high-frequency noise reduction filtering-processing, low-frequency component removal filtering-processing, and logarithmic-image creating processing; and then a system control unit performs control of displaying a moving image of an enlarged image of a set region that is set in the image for display, together with an original image.

Systems and methods are provided to plan a spinal correction surgery. The method includes measuring parameters of a spine in a two-dimensional (2D) spinal image including a thoracic Cobb angle and a thoracic kyphosis (TK) and transforming the 2D image to a three-dimensional (3D), spinal image representation. The transforming includes performing segmentation of spine elements in the 2D image, and applying a formula based on the thoracic Cobb angle and the TK to the spine elements. The method includes identifying a TK goal having a post-operative TK value to selected spine elements, transforming a gap of the spine elements representative of a difference between the pre-operative TK in 3D spinal image representation and the TK goal to create a 3D post-operative spinal image representation, and determining a first rod design based on the 3D post-operative spinal image representation to achieve the post-operative TK value in the spine elements.

A system and method for analyzing images to optimize orthopedic functionality at a site within a patient, including obtaining at least a first, reference image of the site, or a corresponding contralateral site, the first image including at least a first anatomical region or a corresponding anatomical region. At least a second, intra-operative results image of the site is obtained. At least one point is selected to serve as a reference for both images during analysis including at least one of scaling, calculations, and image comparisons.

The disclosure relates, in part, to computer-based visualization of stent position within a blood vessel. A stent can be visualized using intravascular data and subsequently displayed as stent struts or portions of a stent as a part of a one or more graphic user interface(s) (GUI). In one embodiment, the method includes steps to distinguish stented region(s) from background noise using an amalgamation of angular stent strut information for a given neighborhood of frames. The GUI can include views of a blood vessel generated using distance measurements and demarcating the actual stented region(s), which provides visualization of the stented region. The disclosure also relates to display of intravascular diagnostic information such as indicators. An indicator can be generated and displayed with images generated using an intravascular data collection system. The indicators can include one or more viewable graphical elements suitable for indicating diagnostic information such as stent information.

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.

Devices, systems, and methods for stent detection and apposition are disclosed. Embodiments obtain a plurality of images of intravascular image data of a vessel wall and an endovascular device, generate a signal that represents the plurality of images, identify one or more images that correspond to the endovascular device based on the signal that represents the plurality of images, generate a representation of a three-dimensional (3D) shape of the endovascular device based on the one or more images, determine an apposition value of the endovascular device relative to the vessel wall using a representation of a 3D shape of a lumen segment that corresponds to the endovascular device, the apposition value based on a volume difference between the 3D shape of the lumen segment and the 3D shape of the endovascular device, and present information indicating the apposition value.

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.

A method for processing an intravascular image including a plurality of image frames acquired during a pullback of an imaging catheter inserted into a vessel. The method includes obtaining positions of lumen borders detected in the intravascular image and positions of stent-struts detected in the intravascular image. Determining, at different positions in a range, a stent expansion value of the stent implanted in the vessel, based on the first information and the second information, wherein each image frame from image frames in which stent-struts are detected corresponds to a different position along the range. The method may also include displaying an image including positions of lumen borders and positions of the stent-struts detected and a first indicator indicating a level along the range, of the stent expansion value, with the image.

Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

The invention relates to a computer-assisted method for position determination for an intraocular lens supported by machine learning. The method comprises providing a scan result for an eye. The scan result here represents an image of an anatomical structure of the eye. The method further comprises use of a trained machine learning system for the direct determination of a final location of an intraocular lens to be fitted, wherein digital data of the scan of the eye is used as the input data for the machine learning system.