Abstract
Purpose
Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used to locate epileptogenic zones. An accurate identification of the metallic contacts recording the SEEG signal is crucial to ensure effectiveness of the upcoming treatment. However, due to the presence of metal, post-operative CT scans contain strong streak artefacts that interfere with deep learning segmentation algorithms and require a lot of training data to distinguish from actual contacts. We propose a method to generate synthetic data and use them to train a neural network to precisely locate SEEG electrode contacts.
Methods
Random electrodes were generated following manufacturer’s specifications and dimensions and placed in acceptable regions inside metal-free CT images. Metal artefacts were simulated in the generated data set using radon transform, beam hardening, and filtered back projection. A UNet neural network was trained for the contacts segmentation task using various training set-ups combining real data, basic augmented data, and synthetic data. The results were compared.
Results
We reported a higher accuracy when including synthetic data during the network training, while training only on real and basic augmented data more often led to misclassified artefacts or missed contacts. The network segments post-operative CT slices in less than 2 s using 4 GeForce RTX2080 Ti GPUs and in under a minute using a standard PC with GeForce GTX1060.
Conclusion
Using synthetic data to train the network significantly improves contact detection and segmentation accuracy.
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Funding
This work was supported by ArtIC “Artificial Intelligence for Care” grant (ANR-20-THIA-0006-01), co-funded by Région Grand Est, Inria Nancy - Grand Est, IHU Strasbourg, University of Strasbourg and University of Haute-Alsace, France.
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This research study was conducted retrospectively from anonymised data, in accordance with the ethical standards of our institution and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
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Pantovic, A., Ren, X., Wemmert, C. et al. Generation of synthetic training data for SEEG electrodes segmentation. Int J CARS 17, 937–943 (2022). https://doi.org/10.1007/s11548-022-02585-4
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DOI: https://doi.org/10.1007/s11548-022-02585-4