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3D surgical instrument collection for computer vision and extended reality
Gijs Luijten1,2, Christina Gsaxner1, Jianning Li2
1Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria.
Scientific Data
|November 11, 2023
Summary
This study introduces a 3D dataset of 103 medical instruments to advance medical machine learning (MML) and medical mixed reality (MMR) applications in surgery. Publicly available 3D instrument data accelerates research and clinical integration.
Area of Science:
- Medical Engineering
- Computer Vision
- Surgical Technology
Background:
- Advancements in computational hardware and medical machine learning (MML) are enhancing the clinical utility of medical mixed realities (MMR).
- Three-dimensional (3D) datasets of surgical instruments are crucial for accelerating the integration of MML and MMR in clinical practice.
- Existing datasets lack comprehensive 3D models of instruments used in routine clinical settings.
Purpose of the Study:
- To create and release a publicly accessible dataset of 3D-scanned medical instruments.
- To facilitate research and development in medical mixed reality and medical machine learning.
- To encourage the creation and sharing of diverse surgical instrument datasets.
Main Methods:
- Collected 103 3D models of clinically used medical instruments via structured light scanning.
- Utilized 3D software to augment the dataset, creating additional models for analysis.
- Ensured the dataset represents instruments commonly found in surgical procedures.
Main Results:
- A curated collection of 103 3D-scanned medical instruments is now available.
- The dataset includes common surgical tools such as retractors, forceps, and clamps.
- The dataset can be expanded through 3D modeling to increase its size and diversity.
Conclusions:
- The released 3D instrument dataset supports research in instrument detection, tracking, and registration for MMR applications.
- This resource aids in developing virtual reality (VR) and mixed reality (MR) training and simulation scenarios.
- The initiative aims to lower barriers for researchers in the MMR and MML fields.
