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Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition.
Summary
This study introduces advanced Transformer models for surgical phase recognition, achieving state-of-the-art accuracy in both online and offline video analysis. These methods effectively capture temporal dynamics for improved surgical workflow understanding.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Medical Informatics
Background:
- Automatic surgical phase recognition is crucial for operating room efficiency and surgical video analysis.
- Current methods leverage spatial and temporal data, but advancements are needed for enhanced accuracy.
Purpose of the Study:
- To develop novel Transformer-based models for accurate surgical phase recognition.
- To improve the modeling of temporal information at multiple scales for better video analysis.
Main Methods:
- Proposed Multi-Scale Action Segmentation Transformer (MS-AST) for offline and MS-ASCT for online recognition.
- Utilized ResNet50 or EfficientNetV2-M for spatial feature extraction.
- Implemented multi-scale temporal self-attention and cross-attention mechanisms.
Main Results:
- Achieved 95.26% (online) and 96.15% (offline) accuracy on the Cholec80 dataset.
- Established new state-of-the-art results for surgical phase recognition.
- Demonstrated superior performance on non-medical video action segmentation datasets.
Conclusions:
- The proposed MS-AST and MS-ASCT models significantly advance surgical phase recognition.
- These models offer enhanced capture of temporal relationships, leading to state-of-the-art performance.
- The approach is effective for both medical and general video action segmentation tasks.
