A computer-implemented system: at least one processor communicably coupled to at least one nontransitory processor-readable storage medium storing processor-executable instructions or data receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure, and each image set including at least one label which identifies the region of a particular part of the bony structure depicted in each image of the image set, wherein the label indicates one of a plurality of classes indicating parts of the bone anatomy; trains a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received segmentation learning data; and stores the trained segmentation CNN in at least one nontransitory processor-readable storage medium of the machine learning system.

Systems and methods for monitoring one or more anatomical elements are provided. An image depicting one or more anatomical elements and a plurality of annotations may be received. The image may comprise a plurality of image elements. Each annotation may correspond to one of the one or more anatomical elements and may be associated with one of the plurality of image elements. Movement information about movement of at least a portion of a particular anatomical element may be received. Image elements corresponding to the particular anatomical element may be identified to yield a set of image elements. The set of image elements may be adjusted based on the movement information to yield an updated image reflecting changes to affected anatomical elements.

A system for register operating space includes a first positioning mark, a local camera, a second positioning mark, a global camera and a computer system. The first positioning mark is set on a patient. The local camera captures a first image covering the first positioning mark. The second positioning mark is disposed on the local camera. The global camera captures a second image covering the second positioning mark. The focal length of the global camera is shorter than the focal length of the local camera. The computer system is communicatively connected to the local camera and the global camera to provide a navigation interface based on the first image and the second image.

Disclosed is a system and method for operating an assembly, such as a powered drill assembly. The assembly may be operated to provide feedback to the user regarding a selected position and/or condition of the powered drill system. The powered drill system may be used to power or drive a selected tool, such as a resection or grinding tool.

Systems and techniques for determining degree of motion using machine learning to improve medical image quality are presented. In one example, a system generates, based on a convolutional neural network, motion probability data indicative of a probability distribution of a degree of motion for medical imaging data generated by a medical imaging device. The system also determines motion score data for the medical imaging data based on the motion probability data.

A method according to one embodiment of the present disclosure comprises receiving a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements; receiving a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements; determining a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; calculating a homography for each transformation in the set of transformations to yield a set of homographies; and identifying, using the set of homographies, a common portion of each transformation attributable to a change in camera pose, and an individual portion of each transformation attributable to a change in rigid element pose.

An image processing method includes: performing first segmentation processing on an image to be processed, and determining a segmentation region of a target in said image (S11); determining, according to the position of the center point of the segmentation region of the target, an image region where the target is located (S12); and performing second segmentation processing on the image region where each target is located, and determining the segmentation result of the target in said image (S13).

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 vertebra localization and identification method includes: receiving one or more images of vertebrae of a spine; applying a machine learning model on the one or more images to generate three-dimensional (3-D) vertebra activation maps of detected vertebra centers; performing a spine rectification process on the 3-D vertebra activation maps to convert each 3-D vertebra activation map into a corresponding one-dimensional (1-D) vertebra activation signal; performing an anatomically-constrained optimization process on each 1-D vertebra activation signal to localize and identify each vertebra center in the one or more images; and outputting the one or more images, wherein on each of the one or more outputted images, a location and an identification of each vertebra center are specified.

The present invention relates to positioning of an X-ray imaging system. In order to provide an improved relative positioning of the X-ray imaging system for spine interventions, a device (10) for positioning of an X-ray imaging system is provided. The device comprises a data storage unit (12), a processing unit (14) and an output unit (16). The data storage unit is configured to store and provide 3D image data (18) of a spine region of interest of a subject comprising a part of a spine structure, the spine structure comprising at least one vertebra. The processing unit is configured to select at least one vertebra of the spine structure as target vertebra; to segment at least the target vertebra in the 3D image data; wherein the segmentation comprises identifying at least one anatomic feature of the target vertebra; to define a position of a predetermined reference line based on a spatial arrangement of the at least one anatomic feature; and to determine a target viewing direction of an X-ray imaging system based on the reference line. The output unit is configured to provide the target viewing direction for an X-ray imaging system.