SurgPLAN: Surgical Phase Localization Network for Phase Recognition
Xingjian Luo You Pang Zhen Chen Jinlin Wu Zongmin Zhang Zhen Lei Hongbin Liu
Surgical phase recognition is crucial to providing surgery understanding in
smart operating rooms. Despite great progress in automatic surgical phase
recognition, most existing methods are still restricted by two problems. First,
these methods cannot capture discriminative visual features for each frame and
motion information with simple 2D networks. Second, the frame-by-frame
recognition paradigm degrades the performance due to unstable predictions
within each phase, termed as phase shaking. To address these two challenges, we
propose a Surgical Phase LocAlization Network, named SurgPLAN, to facilitate a
more accurate and stable surgical phase recognition with the principle of
temporal detection. Specifically, we first devise a Pyramid SlowFast (PSF)
architecture to serve as the visual backbone to capture multi-scale spatial and
temporal features by two branches with different frame sampling rates.
Moreover, we propose a Temporal Phase Localization (TPL) module to generate the
phase prediction based on temporal region proposals, which ensures accurate and
consistent predictions within each surgical phase. Extensive experiments
confirm the significant advantages of our SurgPLAN over frame-by-frame
approaches in terms of both accuracy and stability.