Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
This work addresses image segmentation challenges in non-invasive imaging for medical and industrial applications, but it is incremental as it builds on existing neural network methods with variations in approach.
The paper tackled the problem of electrical impedance tomography image segmentation from partial boundary data by presenting three data-driven reconstruction methods, including a post-processing approach that achieved first place in the Kuopio tomography challenge 2023, with all methods based on a similar neural network backbone and trained on synthetic data for fair comparison.
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.