Neural Finite-State Machines for Surgical Phase Recognition

2024-11-27

arXiv.org

AI Summary

To address fragmented predictions and the neglect of surgical workflow temporal structure in surgical phase recognition (SPR), this paper proposes a hybrid modeling framework integrating neural networks with a finite state machine (FSM). The method explicitly captures long-range inter-phase dependencies via (1) learnable global state embeddings and a dynamic transition table; (2) a future-phase prediction module leveraging frame repetition padding to enhance temporal consistency; and (3) a plug-and-play design compatible with existing architectures and supporting end-to-end training. Evaluated on the BernBypass70 dataset, the approach achieves state-of-the-art (SOTA) performance: video-level accuracy improves by 0.9%, while phase-level precision, recall, F1-score, and mean average precision (mAP) increase by 3.8, 3.1, 3.3, and 4.1 percentage points, respectively.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Surgical phase recognition (SPR) is crucial for applications in workflow optimization, performance evaluation, and real-time intervention guidance. However, current deep learning models often struggle with fragmented predictions, failing to capture the sequential nature of surgical workflows. We propose the Neural Finite-State Machine (NFSM), a novel approach that enforces temporal coherence by integrating classical state-transition priors with modern neural networks. NFSM leverages learnable global state embeddings as unique phase identifiers and dynamic transition tables to model phase-to-phase progressions. Additionally, a future phase forecasting mechanism employs repeated frame padding to anticipate upcoming transitions. Implemented as a plug-and-play module, NFSM can be integrated into existing SPR pipelines without changing their core architectures. We demonstrate state-of-the-art performance across multiple benchmarks, including a significant improvement on the BernBypass70 dataset - raising video-level accuracy by 0.9 points and phase-level precision, recall, F1-score, and mAP by 3.8, 3.1, 3.3, and 4.1, respectively. Ablation studies confirm each component's effectiveness and the module's adaptability to various architectures. By unifying finite-state principles with deep learning, NFSM offers a robust path toward consistent, long-term surgical video analysis.

Problem

• Improves surgical phase recognition accuracy and temporal coherence.
• Integrates finite-state principles with neural networks for better predictions.
• Enhances workflow optimization and real-time intervention guidance in surgery.

Innovation

• Integrates state-transition priors with neural networks
• Uses learnable global state embeddings for phase identification
• Implements future phase forecasting with frame padding