Synthetic Dataset Generation and Validation for Robotic Surgery Instrument Segmentation

• URL: http://arxiv.org/abs/2602.13844v1
• Date: Sat, 14 Feb 2026 18:29:03 GMT
• Title: Synthetic Dataset Generation and Validation for Robotic Surgery Instrument Segmentation
• Authors: Giorgio Chiesa, Rossella Borra, Vittorio Lauro, Sabrina De Cillis, Daniele Amparore, Cristian Fiori, Riccardo Renzulli, Marco Grangetto,
• Abstract summary: A 3D reconstruction of the Da Vinci robotic arms was refined and animated in Autodesk Maya.<n>To validate the realism and effectiveness of the generated data, several segmentation models were trained under controlled ratios of real and synthetic data.
• Score: 4.731220357458455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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