用自动化流程生成逼真手术机器人器械分割数据集，提升模型泛化能力。
Synthetic Dataset Generation and Validation for Robotic Surgery Instrument Segmentation
- 通过自动化的3D重建与动画生成带标注的逼真视频序列。
- 真实与合成数据均衡训练时，模型泛化性能优于仅用真实数据。
- 适合做手术视觉、数据增强与仿真预训练的研究者使用。
本文提出一套完整的合成数据集生成与验证工作流，用于机器人手术器械分割。通过全自动的Python管道，在Autodesk Maya中对达芬奇机械臂进行3D重建并动画化，生成具有像素级精确标注的逼真视频序列。每个场景融合随机运动模式、光照变化及合成血迹纹理，模拟术中变异性同时保持精确的地面真值掩码。为验证数据真实性和有效性，采用不同比例的真实与合成数据训练多个分割模型。结果表明，真实与合成数据均衡组合显著提升模型泛化能力，而过度依赖合成数据则引入可测量的域偏移。该框架为手术计算机视觉提供可复现、可扩展的工具，支持未来在数据增强、领域自适应和基于仿真的预训练研究。数据与代码见https://github.com/EIDOSLAB/Sintetic-dataset-DaVinci。
原文摘要 · Abstract (English)
This paper presents a comprehensive workflow for generating and validating a synthetic dataset designed for robotic surgery instrument segmentation. A 3D reconstruction of the Da Vinci robotic arms was refined and animated in Autodesk Maya through a fully automated Python-based pipeline capable of producing photorealistic, labeled video sequences. Each scene integrates randomized motion patterns, lighting variations, and synthetic blood textures to mimic intraoperative variability while preserving pixel-accurate ground truth masks. To validate the realism and effectiveness of the generated data, several segmentation models were trained under controlled ratios of real and synthetic data. Results demonstrate that a balanced composition of real and synthetic samples significantly improves model generalization compared to training on real data only, while excessive reliance on synthetic data introduces a measurable domain shift. The proposed framework provides a reproducible and scalable tool for surgical computer vision, supporting future research in data augmentation, domain adaptation, and simulation-based pretraining for robotic-assisted surgery. Data and code are available at https://github.com/EIDOSLAB/Sintetic-dataset-DaVinci.
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