Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition

Automatic surgical phase recognition is a core technology for modern operating rooms and online surgical video assessment platforms. Current state-of-the-art methods use both spatial and temporal information to tackle the surgical phase recognition task. Building on this idea, we propose the Multi-Scale Action Segmentation Transformer (MS-AST) for offline surgical phase recognition and the Multi-Scale Action Segmentation Causal Transformer (MS-ASCT) for online surgical phase recognition. We use ResNet50 or EfficientNetV2-M for spatial feature extraction. Our MS-AST and MS-ASCT can model temporal information at different scales with multi-scale temporal self-attention and multi-scale temporal cross-attention, which enhances the capture of temporal relationships between frames and segments. We demonstrate that our method can achieve 95.26% and 96.15% accuracy on the Cholec80 dataset for online and offline surgical phase recognition, respectively, which achieves new state-of-the-art results. Our method can also achieve state-of-the-art results on non-medical datasets in the video action segmentation domain.

SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition

Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, pre-trained on the target dataset, to extract features from video frames as the training data for SF-TMN. The Slow Path in SF-TMN utilizes all frame features for frame temporal modeling. The Fast Path in SF-TMN utilizes segment-level features summarized from frame features for segment temporal modeling. The proposed paradigm is flexible regarding the choice of temporal modeling networks. We explore MS-TCN and ASFormer models as temporal modeling networks and experiment with multiple combination strategies for Slow and Fast Paths. We evaluate SF-TMN on Cholec80 surgical phase recognition task and demonstrate that SF-TMN can achieve state-of-the-art results on all considered metrics. SF-TMN with ASFormer backbone outperforms the state-of-the-art Not End-to-End(TCN) method by 2.6% in accuracy and 7.4% in the Jaccard score. We also evaluate SF-TMN on action segmentation datasets including 50salads, GTEA, and Breakfast, and achieve state-of-the-art results. The improvement in the results shows that combining temporal information from both frame level and segment level by refining outputs with temporal refinement stages is beneficial for the temporal modeling of surgical phases.