Verb Surgical Inc.

Yiming Xu

Bernhard A Fuerst

Berk Gonenc

Blade Olson

Alexander Barthel

Pablo Garcia Kilroy

Tianyu Song

Margaret F Nicholson

Giacomo Taylor

Mary Lynn Gaddis

Juri Platonov

Genevieve Foley

Lela Dimonte

Wanning Zhu

Jianning Mai

Embodiments described herein provide systems and techniques for tracking workflow inside an operating room (OR) by identifying and tracking target objects, such as a patient bed or a surgical table in the OR. In one aspect, a process for identifying and tracking a target object in an OR begins by receiving a depth image among a sequence of depth images captured by a depth camera installed in the OR. The process then generates a three-dimensional (3D) point cloud based on the depth image. Next, the process identifies a set of potential target points in the 3D point cloud that potentially belongs to the target object based on one or more target object criteria. The process next extracts one or more object clusters of from the set of potential target points using a data-point clustering technique. The process subsequently identifies the target object from the extracted one or more object clusters.

Operating room objects and workflow tracking using depth cameras

A method performed by a surgical system. The method includes receiving a first image from an endoscope, the first image having a surgical instrument at a first location within a field of view of the endoscope. The method matches a three-dimensional (3D) model of the surgical instrument to the first image. The method receives a second image receives a second image from the endoscope, the second image having the surgical instrument at a second location within the field of view of the endoscope, and matches the 3D model of the surgical instrument to the second image. The method estimates a distance between the first and second locations based on both matchings and one or more parameters of the endoscope.

Method and system for estimating positional data in images

Embodiments described herein provide systems and techniques for tracking and de-identifying person in a captured operating room (OR) video. In one aspect, a process for de-identifying OR personnel in an OR video begins by simultaneously receiving a color image captured by an RGB camera and a depth image captured by a depth camera installed in the vicinity of the RGB camera. The process then generates a 3D point cloud based on the depth image. Next, the process applies a human-body detector to the 3D point cloud to detect a set of 3D bodies in the 3D point cloud, wherein each detected 3D body corresponds to a detected person in the OR. The process next projects each detected 3D body into a 2D body outline in the color image to represent the same detected person in the color image. The process subsequently de-identifies the detected people in the color image based on the projected 2D body outlines.

Automatic de-identification of operating room (OR) videos based on depth images

A surgical exercise performed with a surgical robotic system is sensed by depth cameras, generating 3D point cloud data. Robot system data associated with the surgical robotic system is logged. Object recognition is performed on image data produced by the one or more depth cameras, to recognized objects, including surgical equipment and people, in the operating room (OR). The surgical exercise is digitized by storing the 3D point cloud data of unrecognized objects, a position and orientation associated with the recognized objects, and c) the robot system data.

Digitization of an operating room

Disclosed are various real-time robotic-assisted dimension measurement systems and techniques based on the collected kinematic data from one or more surgical tools within a robotic surgical system. In some embodiments, the collected kinematic data can include positions and orientations of each joint of the one or more surgical tools which are derived from positions and orientations of a set of arm joints of one or more robotic arms which the one or more surgical tools are attached to. The disclosed dimension measurement systems allow a surgeon to perform real-time length measurements of both straight-lines and curved-lines, as well as area and volume measurements on specified portions of tissues or organs by strategically placing the tips of the one or more surgical tools over the tissues or organs, while viewing the distal ends of the surgical tools in a user display.

Real-time robotic-assisted dimension measurement system

For control about a remote center of motion (RCM) of a surgical robotic system, possible configurations of a robotic manipulator are searched to find the configuration providing a greatest overlap of the workspace of the surgical instrument with the target anatomy. The force at the RCM may be measured, such as with one or more sensors on the cannula or in an adaptor connecting the robotic manipulator to the cannula. The measured force is used to determine a change in the RCM to minimize the force exerted on the patient at the RCM. Given this change, the configuration of the robotic manipulator may be dynamically updated. Various aspects of this RCM control may be used alone or in combination, such as to optimize the alignment of workspace to the target anatomy, to minimize force at the RCM, and/or to dynamically control the robotic manipulator configuration based on workspace alignment and force measurement.

Remote center of motion control for a surgical robot

A series of images is obtained from an endoscope. Three-dimensional reconstruction is performed on the series of images to reconstruct anatomy shown in the series of images. A graphic, such as a grid, is rendered based on the three-dimensional reconstruction, over the series of images resulting in an enhanced endoscopic video feed to be shown on a display.

3D visualization enhancement for depth perception and collision avoidance

A surgical robotic system that includes a robotic arm, glove configured to be worn on a hand of a user and including a force-feedback mechanism, a tracking device, a processor, and memory. The memory includes instructions which when executed by the processor causes the system to determine that the user is performing a hand gesture with the glove to grasp a virtual user input device (UID) based on the tracking device, and in response to the user grasping the virtual UID, apply, via the force-feedback mechanism, a force upon the glove that corresponds to a physical representation of the virtual UID, and engage the robotic arm to be controlled by the virtual UID.

Growing community of inventors

Sunnyvale, CA, United States of America

Yiming Xu