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: Systems, methods, and non-transitory, machine-readable storage media for imaging a scene are described. In an embodiment, the method includes repeatedly illuminating a scene with illumination light from a plurality of light sources; generating video feed of the scene based on the scene illuminated by the illumination light; illuminating the scene with only individual light sources of the plurality of light sources between illuminating the scene with the plurality of light sources; generating a composite image of the scene based on the scene illuminated by only the individual light sources; and adjusting the composite image based upon input received from the user interface.

Abstract: One example method for scene-adaptive image quality in surgical video includes receiving a first video frame from an endoscope, the first video frame generated from a first raw image captured by an image sensor of the endoscope and processed by an image signal processing (“ISP”) pipeline having a plurality of ISP parameters; recognizing, using a trained machine learning (“ML”) model, a first scene type or a first scene feature type based on the first video frame; determining a first set of ISP parameters based on the first scene type or the first scene feature type; applying the first set of ISP parameters to the ISP pipeline; and receiving a second video frame from the endoscope, the second video frame generated from a second raw image captured by the image sensor and processed by the ISP pipeline using the first set of ISP parameters.

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: 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: 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: 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: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

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: 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: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.