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6-degree vision based tracking of a mandible phantom with deep learning
Citation Link: https://doi.org/10.15480/882.13352
Publikationstyp
Journal Article
Date Issued
2024-09-12
Sprache
English
TORE-DOI
Volume
10
Issue
1
Start Page
5
End Page
8
Citation
Current Directions in Biomedical Engineering 10 (1): 5-8 (2024)
Publisher DOI
Scopus ID
Publisher
De Gruyter
During maxillofacial surgery, the precise placement of surgical tools is crucial for accurate implant placement. Particularly if multiple implants are needed, e.g., after cancerous bone removal, visual landmarks might not be obtainable. To this end, we propose a vision-based tracking approach with deep learning. Our markerless tracking approach is based on video streams from two cameras for tracking a mandible phantom. We study, to what extent vision-based localization using deep learning is feasible. For real-time 6D pose estimation we propose a Siamese network with a ResNet-18 subnetwork. We acquire a large training dataset with a robot and evaluate the tracking accuracy on partially occluded images. Thereby, we mimic visual information that is accessible during clinical interventions. We report a mean position error of 1.33 ± 1.14mm and a rotation error of 0.86 ± 0.71 deg for partially occluded images. Overall we present a promising tracking approach that is marker-free and robust toward image artifacts.
Subjects
Markerless
Maxillofacial Surgery
Medical Phantom
RGB Camera
Tracking
MLE@TUHH
DDC Class
660.6: Biotechnology
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Name
10.1515_cdbme-2024-0102.pdf
Type
Main Article
Size
4.86 MB
Format
Adobe PDF