Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
<|reference_start|>Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data: Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven reconstruction methods for EIT imaging. These three approaches were originally submitted to the Kuopio tomography challenge 2023 (KTC2023). First, we introduce a post-processing approach, which achieved first place at KTC2023. Further, we present a fully learned and a conditional diffusion approach. All three methods are based on a similar neural network as a backbone and were trained using a synthetically generated data set, providing with an opportunity for a fair comparison of these different data-driven reconstruction methods.<|reference_end|>
@article{denker2024data-driven,
    title={Data-driven approaches for electrical impedance tomography image
  segmentation from partial boundary data},
    author={Alexander Denker, Zeljko Kereta, Imraj Singh, Tom Freudenberg, Tobias
  Kluth, Peter Maass, Simon Arridge},
    journal={arXiv preprint arXiv:2407.01559},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01559},
    primaryClass={eess.IV cs.CV cs.LG}
}