Enabling Relevance-Based Exploration of Cataract Videos

Training new surgeons as one of the major duties of experienced expert surgeons demands a considerable supervisory investment of them. To expedite the training process and subsequently reduce the extra workload on their tight schedule, surgeons are seeking a surgical video retrieval system. Automatic workflow analysis approaches can optimize the training procedure by indexing the surgical video segments to be used for online video exploration. The aim of the doctoral project described in this paper is to provide the basis for a cataract video exploration system, that is able to (i) automatically analyze and extract the relevant segments of videos from cataract surgery, and (ii) provide interactive exploration means for browsing archives of cataract surgery videos. In particular, we apply deep-learning-based classification and segmentation approaches to cataract surgery videos to enable automatic phase and action recognition and similarity detection.