Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

A surgical system and method involve a robotic system that has a moveable arm that supports an end effector relative to a surgical site to remove material from a bone. A scanner scans the surgical site and detects a vessel on the bone. Controller(s) is/are coupled to the robotic system and the scanner. The controller(s) obtain image data of the bone and receive, from the scanner, positional information of the vessel on the bone. The controller(s) input the positional information of the vessel to the image data of the bone and generate a boundary for the vessel. The controller(s) register the boundary to the bone for navigation and control the robotic system to remove material from the bone with the end effector and prevent interaction between the end effector and the vessel with the boundary.

An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

The invention relates to a method for automatically registering an object, the method comprising the steps of providing a preoperatively obtained model of the object, providing at least one marker carrier having a plurality of fluoroscopically detectable makers and at least one marker localization element fixed on it, wherein the at least one marker localization element being configured to provide a sensor signal representing position and orientation of the marker localization element in an electromagnetic field, and relative distance and orientation between at least one marker localization element and at least one marker of the plurality of markers are known, arranging the at least one marker carrier on an outer surface of the object, generating at least one fluoroscopic image of at least one marker carrier arranged on the outer surface together with at least one segment of the object in such a way that at least two markers of at least one marker carrier are visible in the generated fluoroscopic image together with at least one segment of the object, determining position and orientation at least of one marker localization element of the arranged marker carrier in an electromagnetic field, and relating image points of the generated fluoroscopic image to model points of said preoperatively obtained model using the determined position and orientation of at least one marker localization element and the known relative distance and orientation between at least one maker localization element and at least one marker of the plurality of markers and/or a known spatial relation between a further marker of the plurality of markers and the at least one marker that has a known relative distance and orientation to at least one maker localization element.

Camera arrays for mediated-reality systems and associated methods and systems are disclosed herein. In some embodiments, a camera array includes a support structure having a center, and a depth sensor mounted to the support structure proximate to the center. The camera array can further include a plurality of cameras mounted to the support structure radially outward from the depth sensor, and a plurality of trackers mounted to the support structure radially outward from the cameras. The cameras are configured to capture image data of a scene, and the trackers are configured to capture positional data of a tool within the scene. The image data and the positional data can be processed to generate a virtual perspective of the scene including a graphical representation of the tool at the determined position.

A device comprises at least one processor and memory including instructions that when executed by the at least one processor cause the at least one processor to: generate, based on at least one image of a spine within a body, a set of possible screw poses for implanting at least one screw into the spine during a surgical procedure; evaluate each possible screw pose based on at least one consideration associated with the surgical procedure; select, based on the evaluation, at least one screw pose from the set of possible screw poses; and output an indication of the selected at least one screw pose to a user interface.

A method of positioning an imaging device relative to a patient, comprising positioning a reference marker adjacent a desired field of scan corresponding to an anatomical element of a patient; and causing an imaging device to align with the reference marker, based on tracking information received from a navigation system, the tracking information corresponding to the reference marker and a navigated tracker disposed on the imaging device.

3D image data of a skeletal portion within a subject's body is acquired. Subsequently, one or more radiopaque elements are positioned with respect to the body and first and second x-rays of the radiopaque elements and the skeletal portion are acquired from respective views. Based upon an identified location of the radiopaque elements within the x-rays, and registration of the x-rays to the 3D image data, the location of the radiopaque elements with respect to the 3D image data is determined. An optical image of the body and the radiopaque elements is acquired and the location of the radiopaque elements within the optical image is identified. The 3D image data is overlaid upon the optical image by aligning (a) the location of the radiopaque elements within the 3D image data with (b) the location of the radiopaque elements within the optical image. Other applications are also described.

A method for autonomous segmentation of three-dimensional nervous system structures from raw medical images, the method including: receiving a 3D scan volume with a set of medical scan images of a region of the anatomy; autonomously processing the set of medical scan images to perform segmentation of a bony structure of the anatomy to obtain bony structure segmentation data; autonomously processing a subsection of the 3D scan volume as a 3D region of interest by combining the raw medical scan images and the bony structure segmentation data, wherein the 3D ROI contains a subvolume of the bony structure with a portion of surrounding tissues, including the nervous system structure; autonomously processing the ROI to determine the 3D shape, location, and size of the nervous system structures by means of a pre-trained convolutional neural network (CNN).

A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.