  Surgical phase recognition has gained significant attention due to its
potential to offer solutions to numerous demands of the modern operating room.
However, most existing methods concentrate on minimally invasive surgery (MIS),
leaving surgical phase recognition for open surgery understudied. This
discrepancy is primarily attributed to the scarcity of publicly available open
surgery video datasets for surgical phase recognition. To address this issue,
we introduce a new egocentric open surgery video dataset for phase recognition,
named EgoSurgery-Phase. This dataset comprises 15 hours of real open surgery
videos spanning 9 distinct surgical phases all captured using an egocentric
camera attached to the surgeon's head. In addition to video, the
EgoSurgery-Phase offers eye gaze. As far as we know, it is the first real open
surgery video dataset for surgical phase recognition publicly available.
Furthermore, inspired by the notable success of masked autoencoders (MAEs) in
video understanding tasks (e.g., action recognition), we propose a gaze-guided
masked autoencoder (GGMAE). Considering the regions where surgeons' gaze
focuses are often critical for surgical phase recognition (e.g., surgical
field), in our GGMAE, the gaze information acts as an empirical semantic
richness prior to guiding the masking process, promoting better attention to
semantically rich spatial regions. GGMAE significantly improves the previous
state-of-the-art recognition method (6.4% in Jaccard) and the masked
autoencoder-based method (3.1% in Jaccard) on EgoSurgery-Phase. The dataset
will be released at https://github.com/Fujiry0/EgoSurgery.
