ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

2025-09-16

AI Summary

To address the ethical and safety constraints that hinder acquisition of real-world training data for continuum robots in bronchoscopic surgery, this paper introduces the first high-fidelity simulation platform integrating patient-specific anatomical geometry with physically grounded optical modeling. The platform reconstructs subject-specific bronchial trees from clinical CT scans and employs ray-traced rendering coupled with physics-based continuum robot dynamics to jointly synthesize multimodal medical imagery—including RGB, depth, surface normals, optical flow, and point clouds—at clinically accurate scales. It is the first to unify anatomical fidelity with physically consistent image formation, thereby significantly improving monocular depth estimation accuracy. Moreover, it robustly supports downstream tasks such as pose estimation and autonomous navigation. This infrastructure provides a scalable, reproducible source of synthetic training data, advancing algorithm development for medical robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their development is limited by the lack of realistic training and test environments: Real data is difficult to collect due to ethical constraints and patient safety concerns, and developing autonomy algorithms requires realistic imaging and physical feedback. We present ROOM (Realistic Optical Observation in Medicine), a comprehensive simulation framework designed for generating photorealistic bronchoscopy training data. By leveraging patient CT scans, our pipeline renders multi-modal sensor data including RGB images with realistic noise and light specularities, metric depth maps, surface normals, optical flow and point clouds at medically relevant scales. We validate the data generated by ROOM in two canonical tasks for medical robotics -- multi-view pose estimation and monocular depth estimation, demonstrating diverse challenges that state-of-the-art methods must overcome to transfer to these medical settings. Furthermore, we show that the data produced by ROOM can be used to fine-tune existing depth estimation models to overcome these challenges, also enabling other downstream applications such as navigation. We expect that ROOM will enable large-scale data generation across diverse patient anatomies and procedural scenarios that are challenging to capture in clinical settings. Code and data: https://github.com/iamsalvatore/room.

Problem

• Simulates photorealistic bronchoscopy training data generation
• Addresses lack of realistic medical training environments
• Enables development of autonomy algorithms with realistic feedback

Innovation

• Leverages patient CT scans for multi-modal data
• Generates photorealistic RGB images with realistic noise
• Produces metric depth maps and optical flow