Intuitive Surgical Operations, Inc.

Siddarth Sen

Brian David Hoffman

Geoffrey A Richmond

Brandon D Itkowitz

Heath Feather

Rohitkumar Godhani

Adam T Schmidt

An exemplary processing system of a computer-assisted surgical system identifies an object in a surgical space, accesses a model of the object, identifies a pose of the object in the surgical space, aligns the model with the pose of the object to define depth data for the model in the surgical space, and generates a depth map in the surgical space for at least a portion of the object based on the depth data for the model. Corresponding systems and methods are also described.

Using model data to generate an enhanced depth map in a computer-assisted surgical system

An exemplary surgical instrument tracking system includes at least one physical computing device that determines, based on endoscopic imagery of a surgical area and using a trained neural network, an observation for an object of interest depicted in the endoscopic imagery, associates, based on a probabilistic framework and kinematics of a robotically-manipulated surgical instrument located at the surgical area, the observation for the object of interest to the robotically-manipulated surgical instrument, and determines a physical position of the robotically-manipulated surgical instrument at the surgical area based on the kinematics of the robotically-manipulated surgical instrument and the observation associated with the robotically-manipulated surgical instrument.

Systems and methods for tracking a position of a robotically-manipulated surgical instrument

An imaging system is provided for pixel-level segmentation of images comprising: a camera to capture images of an anatomical object and to represent the images in two-dimensional (2D) arrangements of pixels; one or more processors and a non-transitory computer readable medium with information including: CNN instructions to cause the one or more processors to implement a CNN configured to associate anatomical object classifications with pixels of the 2D arrangements of pixels; and multiple sets of weights, to differently configure the CNN based upon different camera image training data; and a display screen configured to display the two-dimensional (2D) arrangements of classified pixels and the anatomical object classifications.

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San Jose, CA, United States of America

Siddarth Sen