Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data.
To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1–L5 from ten cadaveric specimens.
We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm.
In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6.
Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.
Citation:
Liebmann F, Stütz D, Suter D, Jecklin S, Snedeker JG, Farshad M, Fürnstahl P, Esfandiari H.
SpineDepth: A multi-modal data collection approach for automatic labelling and intraoperative spinal shape reconstruction based on RGB-D data.
J Imaging. 2021 Aug 27;7(9):164.