Latus S, Sprenger J, Neidhardt M, Schadler J, Ron A, Fitzek A, Schluter M, Breitfeld P, Heinemann A, Puschel K, Schlaefer A. Rupture Detection During Needle Insertion Using Complex OCT Data and CNNs.
IEEE Trans Biomed Eng 2021;
68:3059-3067. [PMID:
33651681 DOI:
10.1109/tbme.2021.3063069]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
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.
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