A reproducible framework for synthetic data generation and instance segmentation in robotic suturing
This study demonstrates the potential of sim-to-real synthetic datasets to advance robotic suturing automation through a simple and reproducible framework. By sharing 3D models, Unity environments and annotated datasets, this work provides resources for creating additional images, expanding datasets, and enabling fine-tuning or semi-supervised learning. By facilitating further exploration, this work lays a foundation for advancing suturing automation and addressing task-specific dataset scarcity.
Authors
- Francesco Marzola (ORCID: https://orcid.org/0000-0002-9675-3220)
- Matteo Pescio (ORCID: https://orcid.org/0009-0001-3035-1392)
- Maura Casadio (ORCID: https://orcid.org/0000-0003-2338-8995)
- Alberto Arezzo (ORCID: https://orcid.org/0000-0002-2110-4082)
- Pietro Leoncini
- Giulio Dagnino (ORCID: https://orcid.org/0000-0002-1613-1051)
Institutions
- Politecnico di Torino (IT)
- University of Turin (IT)
- University of Genoa (IT)
- University of Twente (NL)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1007/s11548-025-03460-8
- Citations
- 1
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.67