Rupture detection during needle insertion using complex OCT data and CNNs
| Autor: | Philipp Breitfeld, Axel Heinemann, Matthias Schlüter, Alexander Schlaefer, Johanna Sprenger, Sarah Latus, Julia Schädler, Maximilian Neidhardt, Antonia Fitzek, Alexandra Ron, Klaus Püschel |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Biomedical Engineering Ingenieurwissenschaften [620] relative tissue motion Optical coherence tomography ddc:570 medicine Computer vision ddc:610 Technik [600] Mechanical Phenomena optical coherence tomography medicine.diagnostic_test business.industry Phantoms Imaging 600: Technik Biowissenschaften Biologie [570] Deep learning Robotics 570: Biowissenschaften Biologie 620: Ingenieurwissenschaften Medizin [610] Needles Needle placement Needle insertion 610: Medizin Artificial intelligence needle navigation ddc:620 business ddc:600 Tomography Optical Coherence |
| Zdroj: | IEEE Transactions on Biomedical Engineering 68 (10): 3059-3067 (2021-10) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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