Abstract: A simulator for simulating soft body deformation includes a display system, a user interface, and a controller. The controller includes one or more processors coupled to memory that stores instructions that when executed cause the system to perform operations. The operations include generating a simulated soft body having a shape represented, at least in part, by a plurality of nodes. The operations further include determining, with at least a first machine learning model, a displacement of individual nodes included in the plurality of nodes in response to a simulated force applied to the simulated soft body. The operations further include rendering a deformation of the simulated soft body in substantially real time in response to the simulated force based, at least in part, on the displacement.

Abstract: A surgical simulator for simulating a surgical scenario comprises a display system, a user interface, and a controller. The controller includes one or more processors coupled to memory that stores instructions that when executed cause the system to perform operations. The operations include generating simulated surgical videos, each representative of the surgical scenario. The operations further include associating simulated ground truth data from the simulation with the simulated surgical videos. The ground truth data corresponds to context information of at least one of a simulated surgical instrument, a simulated anatomical region, a simulated surgical task, or a simulated action. The operations further include annotating features of the simulated surgical videos based, at least in part, on the simulated ground truth data for training a machine learning model.

Abstract: A system includes a display, and a database including surgical videos, images of organs in a human body obtained from a medical imaging device, and images of disease in a human body obtained from the medical imaging device. A controller including a processor is coupled to memory, the database, and the display, and the memory stores information that when executed by the processor causes the system to perform operations. For example, the processor may determine first organ information from the images of the organs, and first disease information from the images of the disease. The processor my calculate a similarity score between the first organ information and the first disease information and second disease information and second organ information indexed to the surgical videos. The processor selects one or more of the surgical videos based on the similarity score, and displays the surgical videos on the display.

Abstract: A system for robot-assisted surgery includes an image sensor and a display. The system further includes a controller coupled to the image sensor and the display, where the controller includes logic that when executed by the controller causes the system to perform operations. The operations may include acquiring first images of a surgical procedure with the image sensor, and analyzing the first images with the controller to identify a surgical step in the surgical procedure. The operations may further include displaying second images on the display in response to identifying the surgical step; the second images may include at least one of a diagram of human anatomy, a preoperative image, an intraoperative image, or an annotated image of one of the first images.

Abstract: Systems and methods for segmenting surgical videos are disclosed. One example method includes receiving, by a processor of a computing device, surgical video, the surgical video comprising at least a sequence of video frames of a surgical procedure; in response to receiving an identification of a video frame, generating, by the processor, a bookmark based on the video frame; associating, by the processor, the bookmark with the video frame; and storing, by the processor, the bookmark in a non-transitory computer-readable medium.

Abstract: A system for video-assisted surgery includes one or more displays, a memory including at least one preoperative image, and a camera coupled to capture a video. A controller is coupled to the memory, the camera, and the one or more displays, and the controller includes logic that when executed by the controller causes the system to perform a variety of operations. The system may capture a video of a surgical area, including anatomical features, using the camera, and display the video of the surgical area on the one or more displays. The system may also display the at least one preoperative image on the one or more displays at the same time as the video. The location of the anatomical features shown in the video is displayed as an accentuated region on the at least one preoperative image.

Abstract: A system for robotic surgery includes a surgical robot with one or more arms, where at least some of the arms in the one or more arms holds a surgical instrument. An image sensor is coupled to capture a video of a surgery performed by the surgical robot, and a display is coupled to receive an annotated video of the surgery. A processing apparatus is coupled to the surgical robot, the image sensor, and the display. The processing apparatus includes logic that when executed by the processing apparatus causes the processing apparatus to perform operations including identifying anatomical features in the video using a machine learning algorithm, and generating the annotated video. The anatomical features from the video are accentuated in the annotated video. The processing apparatus also outputs the annotated video to the display in real time.

Abstract: An apparatus, system and process for guiding a surgeon during a medical procedure to prevent surgical mistakes are described. The system may include a machine learning medical procedure server that generates one or more machine learning medical procedure models using, at least, medical procedure data captured during medical procedures performed at a plurality of different medical procedure systems. The system may also include a medical procedure system communicably coupled with the machine learning medical procedure server that receives a selected machine learning medical procedure model from the machine learning medical procedure server, and utilizes the selected machine learning medical procedure model during a corresponding medical procedure to control one or more operations of the medical procedure system.

Abstract: A system, method and user interface for interactive mesh painting of 2D images for iterative high quality 3D modeling is disclosed herein. The system takes a set of calibrated 2D images as input and provides an intuitive user interface based on simple interactive 2D painting operations. The output is a textured, high quality 3D model that on average is obtained after just a few minutes of interaction. This can be achieved by utilizing only a minimum number of different modes (panning, zooming, painting) when interacting with the 2D images. In an embodiment, a component of the system is a GPU-based multi-view stereo reconstruction scheme, which is implemented by an incremental reconstruction algorithm, that runs in the background during user interaction with a 2D image so that the user does not notice any significant response delay in generation of the corresponding modeled 3D surface.