Denker, Alexander;
Kereta, Željko;
Singh, Imraj;
Freudenberg, Tom;
Kluth, Tobias;
Maass, Peter;
Arridge, Simon;
(2024)
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data.
Applied Mathematics for Modern Challenges
, 2
(2)
pp. 119-139.
10.3934/ammc.2024005.
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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, that were submitted to the Kuopio tomography challenge 2023 (KTC2023). First, we introduce a post-processing method, 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 backbone and were trained using a synthetically generated data set, providing a fair comparison of these different data-driven reconstruction methods.
| Type: | Article |
|---|---|
| Title: | Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3934/ammc.2024005 |
| Publisher version: | http://dx.doi.org/10.3934/ammc.2024005 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Inverse problems, deep learning, electrical impedance tomography |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193844 |
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