Respective longitudinal insertion paths for two tools are planned and associated with 3D image data of a skeletal portion. While respective portions of the tools are disposed at first respective locations along their respective longitudinal insertion paths, first and second x-rays of the respective portions of the tools and the skeletal portion are acquired from respective first and second views. A computer processor matches between a tool in the first x-ray and the same tool in the second x-ray by: (A) identifying respective tool elements of each tool within the first and second x-rays, (B) registering the first and second x-rays to the 3D image data, and (C) based upon the identified respective tool elements and the registration, identifying for at least one tool element a correspondence between the tool element and the respective planned longitudinal insertion path for that tool. Other applications are also described.

An ultrasound probe for percutaneous insertion into an incision and related methods are disclosed herein, e.g., for imaging neural structures at a surgical site of a patient. An exemplary ultrasound probe can be a portable ultrasound probe configured to be passed percutaneously into an incision and can have an imaging region extending distally from a distal tip of the probe. In one embodiment the ultrasound probe can be a navigated portable ultrasound probe. The ultrasound probe can be connected to a computing station and configured to transmit images to the computing station for processing. In another embodiment, an ultrasound probe can be part of a network of sensors, including at least one external sensor, where the network of sensors is configured to transmit images to the computing station for processing. The computing station can process and display images to visualize and/or highlight neurological structures in an imaged region.

Provided are a method and an apparatus for analyzing a body composition by using a medical image. The apparatus for analyzing the body composition trains a first artificial intelligence model by using training data labelling a body tissue region, such as muscle or fat, in a medical image for training, and then, segments a body tissue from an examination medical image by using the first artificial intelligence model and outputs body composition information based on a region, a volume, or a weight of the body tissue of the examination medical image. The disclosure is a technique developed through the Seoul Business Agency's 2020 support project for artificial intelligence (AI) technology industrialization (CY20053), “AI Verification and Industrialization of Computed Tomography (CT) Image-based Opportunistic Screening of Metabolic Syndrome, Osteoporosis, and Muscle Reduction.”

A system for executing a three-dimensional (3D) intraoperative scan of a patient is disclosed. A 3D scanner controller projects the object points included onto a first image plane and the object points onto a second image plane. The 3D scanner controller determines first epipolar lines associated with the first image plane and second epipolar lines associated with the second image plane based on an epipolar plane that triangulates the object points included in the first 2D intraoperative image to the object points included in the second 2D intraoperative image. Each epipolar lines provides a depth of each object as projected onto the first image plane and the second image plane. The 3D scanner controller converts the first 2D intraoperative image and the second 2D intraoperative image to the 3D intraoperative scan of the patient based on the depth of each object point provided by each corresponding epipolar line.

Methods and systems for intraoperatively determining alignment parameters of a spine during a spinal surgical procedure are disclosed herein. In some embodiments, a method of intraoperatively determining an alignment parameter of a spine during a surgical procedure includes receiving initial image data of the spine including multiple vertebrae and identifying a geometric feature associated with each vertebra in the initial image data. The geometric features each have a pose in the initial image data and characterize a three-dimensional (3D) shape of the associated vertebra. The method further comprises receiving intraoperative image data of the spine and registering the initial image data to the intraoperative image data. The method can then update the pose of each geometric feature based on the registration and the intraoperative image data, and determine the alignment parameter based on the updated poses of the geometric features associated with two or more of the vertebrae.

Methods of generating three-dimensional models of musculoskeletal systems, and three-dimensional bone and soft tissue model reconstruction, and associated apparatus, are disclosed. An example method of generating a virtual 3-D patient-specific bone model may include obtaining a preliminary 3-D bone model of a first bone; obtaining a supplemental image of the first bone; registering the preliminary 3-D bone model of the first bone with the supplemental image of the first bone; extracting geometric information about the first bone from the supplemental image of the first bone; and/or generating a virtual 3-D patient-specific bone model of the first bone by refining the preliminary 3-D bone model of the first bone using the geometric information about the first bone from the supplemental image of the first bone.

A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

The present invention relates to a method for measuring muscle mass, including: a first selection step, wherein a frame selection information is obtained by using a frame to select a fascia region from a provided computed tomography image under the condition that the window width ranges from 300 HU to 500 HU and the window level ranges from 40 HU to 50 HU, wherein the selected range of the fascia region includes a muscle; and a second selection step, wherein a muscle information of the muscle is obtained by calculating a pixel value in the frame-selected fascia region under the condition that the HU value of the CT image ranges from -29 HU to 150 HU.

A surgical system and method of operating the same involve a surgical operating table is controllable and adjustable and that supports a patient thereon. A robotic system includes a moveable arm that supports and moves an end effector relative to a surgical site of the patient. Controller(s) coupled to the surgical operating table and to the robotic system associate a virtual boundary with respect to the surgical site and detect that the end effector is outside of the virtual boundary. In response to detection of the end effector being outside of the virtual boundary, the controller(s) command adjustment of the surgical operating table to reposition the surgical site and the virtual boundary to enable the end effector to be inside the virtual boundary.

The present disclosure directs to a system and method for image processing. The method for image processing comprises acquiring a plurality of original computed tomography (CT) images of a spine of a subject; generating CT value images of the spine of the subject by processing the plurality of original CT images. The method further includes identifying an optimal sagittal image in which a centerline of the spine is located based on the CT value images. The method further includes identifying the centerline of the spine within the optimal sagittal image. The method further includes identifying a center point and a direction of at least one intervertebral disc along the centerline of the spine. The method still further includes reconstructing an image of the at least one intervertebral disc based on the center point and the direction of the at least one intervertebral disc.