Abstract: Embodiments described herein provide a surgical duration estimation system for continuously predicting real-time remaining surgical duration (RSD) of a live surgical session of a given surgical procedure based on a real-time endoscope video of the live surgical session. In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples N?1 additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session between the beginning of the endoscope video corresponding to the beginning of the live surgical session and the current frame corresponding to the current time. The process then combines the N?1 randomly sampled frames and the current frame in the temporal order to obtain a set of N frames.

Abstract: Disclosed are various systems and techniques for performing video-based surgeon technical-skill assessments and classifications. In one aspect, a process for classifying a surgeon's technical skill in performing a surgery is disclosed. During operation, the process receives a tool-motion track comprising a sequence of detected tool motions of a surgeon performing a surgery with a surgical tool. The process then generates a sequence of multi-channel feature matrices to mathematically represent the tool-motion track. Next, the process performs a one-dimensional (1D) convolution operation on the sequence of multi-channel feature matrices to generate a sequence of context-aware multi-channel feature representations of the tool-motion track.

Abstract: Disclosed are various systems and techniques for tracking surgical tools in a surgical video. In one aspect, the system begins by receiving one or more established tracks for one or more previously-detected surgical tools in the surgical video. The system then processes a current frame of the surgical video to detect one or more objects using a first deep-learning model. Next, for each detected object in the one or more detected objects, the system further performs the flowing steps to assign the detected object to a right track: (1) computing a semantic similarity between the detected object and each of the one or more established tracks; (2) computing a spatial similarity between the detected object and the latest predicted location for each of the one or more established tracks; and (3) attempting to assign the detected object to one of the one or more established tracks based on the computed semantic similarity and the spatial similarity metric.

Abstract: Embodiments described herein provide a surgical duration estimation system for continuously predicting real-time remaining surgical duration (RSD) of a live surgical session of a given surgical procedure based on a real-time endoscope video of the live surgical session. In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples N-1 additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session between the beginning of the endoscope video corresponding to the beginning of the live surgical session and the current frame corresponding to the current time. The process then combines the N-1 randomly sampled frames and the current frame in the temporal order to obtain a set of N frames.

Abstract: Techniques and systems are provided for processing one or more low light images. For example, a short exposure image associated with one or more shutter speeds can be obtained. A long exposure image is also obtained, which is captured using a slower shutter speed than the one or more shutter speeds associated with the short exposure image. An output image can be generated by mapping color information from the long exposure image to the short exposure image.

Abstract: Techniques and systems are provided for processing one or more low light images. For example, a short exposure image associated with one or more shutter speeds can be obtained. A long exposure image is also obtained, which is captured using a slower shutter speed than the one or more shutter speeds associated with the short exposure image. An output image can be generated by mapping color information from the long exposure image to the short exposure image.