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.

翻译：手术阶段识别对于实现智能手术室中的手术理解至关重要。尽管自动手术阶段识别取得了显著进展，但现有方法仍受限于两个问题。首先，这些方法无法通过简单的2D网络捕捉每帧的判别性视觉特征及运动信息。其次，逐帧识别范式因每个阶段内的预测不稳定（即阶段抖动）而降低了性能。为解决这两个挑战，我们提出了一种基于时间检测原理的手术阶段定位网络SurgPLAN，以实现更准确且稳定的手术阶段识别。具体而言，我们首先设计了一个金字塔慢速快速（Pyramid SlowFast, PSF）架构作为视觉主干，通过采用不同帧采样率的两个分支捕捉多尺度时空特征。此外，我们提出了一个时间阶段定位（Temporal Phase Localization, TPL）模块，基于时间区域提议生成阶段预测，从而确保每个手术阶段内预测的准确性和一致性。大量实验证实，我们的SurgPLAN在准确性和稳定性方面均显著优于逐帧方法。