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Rupture detection during needle insertion using complex OCT data and CNNs
Citation Link: https://doi.org/10.15480/882.3808
Publikationstyp
Journal Article
Date Issued
2021-10
Sprache
English
TORE-DOI
TORE-URI
Volume
68
Issue
10
Start Page
3059
End Page
3067
Citation
IEEE Transactions on Biomedical Engineering 68 (10): 3059 - 3067 (2021-10)
Publisher DOI
Scopus ID
Publisher
IEEE
Objective: Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue interfaces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, whenever ultrasound (US) image guidance is hard to align and externally sensed forces are superimposed by friction.
Methods: We propose an experimental setup for reproducible needle insertions, applying optical coherence tomography (OCT) directly at the needle tip as well as external US and force measurements. Processing the complex OCT data is challenging as the penetration depth is limited and the data can be difficult to interpret. Using a machine learning approach, we show that ruptures can be detected in the complex OCT data without additional external guidance or measurements after training with multi-modal ground-truth from US and force.
Results: We can detect ruptures with accuracies of 0.94 and 0.91 on homogeneous and inhomogeneous phantoms, respectively, and 0.71 for ex-situ tissues.
Conclusion: We propose an experimental setup and deep learning based rupture detection for the complex OCT data in front of the needle tip, even in deeper tissue structures without the need for US or force sensor guiding. Significance: This study promises a suitable approach to complement a robust robotic needle placement
Methods: We propose an experimental setup for reproducible needle insertions, applying optical coherence tomography (OCT) directly at the needle tip as well as external US and force measurements. Processing the complex OCT data is challenging as the penetration depth is limited and the data can be difficult to interpret. Using a machine learning approach, we show that ruptures can be detected in the complex OCT data without additional external guidance or measurements after training with multi-modal ground-truth from US and force.
Results: We can detect ruptures with accuracies of 0.94 and 0.91 on homogeneous and inhomogeneous phantoms, respectively, and 0.71 for ex-situ tissues.
Conclusion: We propose an experimental setup and deep learning based rupture detection for the complex OCT data in front of the needle tip, even in deeper tissue structures without the need for US or force sensor guiding. Significance: This study promises a suitable approach to complement a robust robotic needle placement
Subjects
Deep learning
needle navigation
optical coherence tomography
relative tissue motion
MLE@TUHH
DDC Class
570: Biowissenschaften, Biologie
600: Technik
610: Medizin
620: Ingenieurwissenschaften
More Funding Information
The authors acknowledge support for theOpen Access fees by Hamburg University of Technology (TUHH) in the funding programme
Open Access Publishing.
Open Access Publishing.
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