Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes

2025-10-28

AI Summary

To address the lack of spatiotemporal consistency in multimodal perception during surgical procedures, this paper proposes the first 4D audio-visual joint modeling framework tailored for dynamic surgical environments. It achieves high-precision 3D sound source localization via a phase-based microphone array and establishes spatiotemporal alignment with dynamic point clouds captured by an RGB-D camera. A Transformer-based cross-modal fusion architecture is designed to jointly support acoustic event detection, audio-visual feature alignment, and fine-grained spatiotemporal mapping of instrument–tissue interactions. Evaluated in realistic simulated surgical settings, the method achieves centimeter-level 3D acoustic localization accuracy and robust association with visual semantic elements—marking the first demonstration of such capability. It significantly enhances spatiotemporal consistency in multimodal representations and improves surgical action parsing precision.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Purpose: Surgical scene understanding is key to advancing computer-aided and intelligent surgical systems. Current approaches predominantly rely on visual data or end-to-end learning, which limits fine-grained contextual modeling. This work aims to enhance surgical scene representations by integrating 3D acoustic information, enabling temporally and spatially aware multimodal understanding of surgical environments. Methods: We propose a novel framework for generating 4D audio-visual representations of surgical scenes by projecting acoustic localization information from a phased microphone array onto dynamic point clouds from an RGB-D camera. A transformer-based acoustic event detection module identifies relevant temporal segments containing tool-tissue interactions which are spatially localized in the audio-visual scene representation. The system was experimentally evaluated in a realistic operating room setup during simulated surgical procedures performed by experts. Results: The proposed method successfully localizes surgical acoustic events in 3D space and associates them with visual scene elements. Experimental evaluation demonstrates accurate spatial sound localization and robust fusion of multimodal data, providing a comprehensive, dynamic representation of surgical activity. Conclusion: This work introduces the first approach for spatial sound localization in dynamic surgical scenes, marking a significant advancement toward multimodal surgical scene representations. By integrating acoustic and visual data, the proposed framework enables richer contextual understanding and provides a foundation for future intelligent and autonomous surgical systems.

Problem

• Integrating 3D acoustic data to enhance surgical scene understanding
• Localizing surgical acoustic events in 3D space with visual elements
• Creating dynamic multimodal representations for intelligent surgical systems

Innovation

• Integrates 3D acoustic information with visual data
• Projects acoustic localization onto dynamic point clouds
• Uses transformer-based acoustic event detection module