Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data

Alexander Denker, Zeljko Kereta, Imraj Singh, Tom Freudenberg, Tobias Kluth +2 more
5/6/2024
eess.IVcs.CVcs.LG

No official code found. Best implementation: alexdenker/eit_fenicsx (10 stars, MIT, last commit 2025-04-14).

Abstract

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.

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Code Implementations(1)

alexdenker/eit_fenicsxCommunity100%
MIT

Implementation of the CEM in EIT used for KTC2023

100PythonDec 12, 20231 years agoMIT

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Cite this paper

@article{denker2024datadriven,
  title  = {Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data},
  author = {Alexander Denker and Zeljko Kereta and Imraj Singh and Tom Freudenberg and Tobias Kluth and Peter Maass and Simon Arridge},
  year   = {2024},
  eprint = {2407.01559},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.01559v1}
}

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