3D-COSI ~ 3D Collection of Surgical Instruments
Bibliographic record
Abstract
COSI - 3D STL Collection of Surgical Instruments Due to large file names, we have chosen to use https://www.7-zip.org/ which is 100% free and compatible with WinZip. If you encounter an error using WinZip, it's likely due to large file names, please use 7zip. Inside the repository, you will find an information overview "Overview.docx", a showcase video "Example video 3D instruments.mp4", STL files of 103 surgical instruments "Surgical Instruments.7z", examples of variations of the surgical instruments using Blender add-on or Python script build on the Trimesh library "Blender Part x of 8 ... 7z", or "Trimesh part x of 9 ... .7z". You will also find the used Blender Add On "MultiMesh.zip", measurements of virtual instruments and settings for the add-on (.xlsx), and the script that was used to perform these measurements inside a single folder, "Scripts, measurements and used Blender settings.7z". The proposed data collection consists of 103 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments like retractors, forceps, and clamps. The collection is augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality or mixed reality.Related article:Luijten, G., Gsaxner, C., Li, J. et al. 3D surgical instrument collection for computer vision and extended reality. Sci Data 10, 796 (2023). https://doi.org/10.1038/s41597-023-02684-0
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".