i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery - IPCAI short presentation
Channel: David Kügler
Date: 2020-06-21

The short presentation to The 11th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2020).

Kügler, D; Sehring, J; Stefanov, A; Stenin, I; Kristin, J; Klenzner, T; Schipper, J; Mukhopadhyay, A (2020): i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery. IPCAI 2020 and IJCARS, Springer, doi: 10.1007/s11548-020-02157-4


You can find more information to our work at https://i3posnet.david-kuegler.de/


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The paper is also available as an Open Access publication both at Springer https://link.springer.com/article/10.1007/s11548-020-02157-4 and at arXiv https://arxiv.org/abs/1802.09575 



Abstract
Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image.

Methods: i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations.



Results: We show i3PosNet reaches errors less than 0.05 mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real X-rays without any further adaptation.



Conclusion: The translation of deep learning-based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.