Deep-learning-based instrument detection for intra-operative robotic assistance

verfasst von
Jorge Badilla Solórzano, Svenja Spindeldreier, Sontje Ihler, Nils-Claudius Gellrich, Simon Spalthoff
Abstract

Purpose:: Robotic scrub nurses have the potential to become an attractive solution for the operating room. Surgical instrument detection is a fundamental task for these systems, which is the focus of this work. We address the detection of the complete surgery set for wisdom teeth extraction, and propose a data augmentation technique tailored for this task. Methods:: Using a robotic scrub nurse system, we create a dataset of 369 unique multi-instrument images with manual annotations. We then propose the Mask-Based Object Insertion method, capable of automatically generating a large amount of synthetic images. By using both real and artificial data, different Mask R-CNN models are trained and evaluated. Results:: Our experiments reveal that models trained on the synthetic data created with our method achieve comparable performance to that of models trained on real images. Moreover, we demonstrate that the combination of real and our artificial data can lead to a superior level of generalization. Conclusion:: The proposed data augmentation technique is capable of dramatically reducing the labelling work required for training a deep-learning-based detection algorithm. A dataset for the complete instrument set for wisdom teeth extraction is made available for the scientific community, as well as the raw information required for the generation of the synthetic data (https://github.com/Jorebs/Deep-learning-based-instrument-detection-for-intra operative-robotic-assistance).

Organisationseinheit(en)
Institut für Mechatronische Systeme
Externe Organisation(en)
Medizinische Hochschule Hannover (MHH)
Typ
Artikel
Journal
International journal of computer assisted radiology and surgery
Band
17
Seiten
1685-1695
Anzahl der Seiten
11
ISSN
1861-6410
Publikationsdatum
09.2022
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Chirurgie, Biomedizintechnik, Radiologie, Nuklearmedizin und Bildgebung, Maschinelles Sehen und Mustererkennung, Angewandte Informatik, Gesundheitsinformatik, Computergrafik und computergestütztes Design
Elektronische Version(en)
https://doi.org/10.1007/s11548-022-02715-y (Zugang: Offen)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463311/ (Zugang: Offen)