Methods and systems for selecting an appropriate prosthesis for a prospective implant recipient are discussed. For example, a method for selecting an appropriate prosthesis can include, accessing anatomical image data, receiving an indication of a plurality of landmark locations within the image data, constructing a femoral canal axial indication within the image data, producing a plurality of lateral anatomic structural measurements, and selecting the appropriate prosthesis to fit the prospective implant recipient. The anatomical image data can include sufficient detail to allow measurement of internal and external geometry of a proximal femur. The plurality of lateral anatomic structural measurements can include measurements along, and perpendicular to, the femoral canal axial indication that run along a femoral canal within the anatomical image data. The prosthesis can be selected based, at least in part, on the plurality of lateral anatomic structural measurements.

Techniques are described for determining a pre-morbid shape of an anatomical object. A method includes receiving first image data of a first anatomical structure and second image data of a second anatomical structure. The first and second anatomical structures are anatomically related. The method includes determining a first shape model based on the first image data and a joint statistical shape model (SSM). The method includes determining a second shape model based on the first shape model, the first image data, and the second image data, the second shape model including a second estimated shape of the first anatomical structure and a second estimated shape for the second anatomical structure. The method includes generating anatomical information indicative of the pre-morbid shape of at least the second anatomical structure based on the second shape model.

Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

A navigated surgery system includes at least one processor that is operative to obtain a 2D medical image slice of anatomical structure of a patient. The operations further obtain a 3D graphical model of anatomical structure. The operations determine a pose of a virtual cross-sectional plane extending through the 3D graphical model of the anatomical structure that corresponds to the anatomical structure of the 2D medical image slice. The operations control the XR headset to display the 2D medical image slice of the anatomical structure of the patient, display the 3D graphical model of the anatomical structure, and display a graphical object oriented with the pose relative to the 3D graphical model of the anatomical structure.

A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

One variation of a method for augmenting a surgical field with virtual guidance content includes: accessing a scan representing a tissue of a patient; combining the scan with a generic virtual anatomical model to define a custom virtual anatomical model of the tissue; defining a cut trajectory along an intersection between a virtual model of a surgical implant and the custom virtual anatomical model of the tissue; aligning a virtual cut surface to the cut trajectory to locate the virtual model of the surgical guide relative to the custom virtual anatomical model; accessing an image of a surgical field; detecting the tissue in the image; aligning the custom virtual anatomical model to the tissue detected in the image; defining a target real location for a real surgical guide in the surgical field; and generating a frame depicting the target real location of the surgical guide in the surgical field.

Methods and systems are described which allow a classification of first and second objects in an X-ray projection image. A respective representation and localization of both objects are determined by applying the models to match the objects in the X-ray image and a spatial relation of the classified objects is obtained. Such methods and systems take advantage of artificial intelligence.

In an X-ray diagnostic apparatus of one embodiment, an image data generator sequentially generates X-ray images based on X-rays transmitted through a subject. An image processor executes: first processing where, in response to an instruction to start correction processing, a position of a target contained in a predetermined X-ray image is obtained as a reference position; and second processing where corrected images in which positions of the target are set at the reference position are sequentially generated from newly generated X-ray images. An image data storage unit stores therein information on a reference position with respect to each set of conditions of manipulation on the subject. Upon receiving the instruction to start correction processing, the image processor executes the second processing by using information on the reference position stored in the image data storage unit, in accordance with a set of the conditions of manipulation on the subject.

A method of generating a template image includes: receiving an input from a user representing identifications of an object in different respective slices of a volumetric image; using the input to determine a volume-of-interest (VOI) that includes voxels in a subset of the volumetric image; and determining the template image using at least some of the voxels in the VOI, wherein the act of determining the template image comprises performing a forward projection of the at least some of the voxels in the VOI using a processor. An image processing method includes: obtaining a volumetric image; performing forward projection of voxels in the volumetric image from different positions onto a first plane using a processor; and summing projections on the first plane resulted from the forward projection from the different positions to create a first image slice in the first plane.