DOI: 10.5281/zenodo.8252369
title: Kuopio Tomography Challenge 2023 open electrical impedance tomographic dataset (KTC 2023)

description: Notice: v3 update 17.4.2023. Ground truth images were corrected slightly. The older ground truth images had a geometric distortion caused by cropping the photographs to the outer boundary of the imaging chamber, instead of the inner boundary on which the measurement electrodes were located. In addition, version 3 now contains both the limited EIT data used in the challenge evaluation, as well as the full EIT data with no measurements left out.

It is recommended to download version v3 and replace the v2 version with it if using the data for research purposes.

This dataset is primarily designed for the Kuopio Tomography Challenge 2023 (KTC 2023), but it can be used for generic algorithm research and development in 2D Electric Impedance Tomography (EIT) image reconstruction. The purpose of KTC 2023 is to develop algorithms for electric impedance tomography image reconstruction with limited data.

The dataset contains electric impedance tomography data collected from phantoms made up of a cylindrical water chamber with inclusions of varying shapes and sizes present. The data consists of electrical measurements collected using electrodes placed at the chamber boundary, i.e. the values of electric current injected between two electrodes at a time, and the resulting voltages between adjacent electrode pairs. The data has been stored in the MATLAB .mat format.

The training dataset contains data measured from five training phantoms. These are designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contained inclusions, which were either resistive or conductive in comparison to the water in the imaging chamber. The fifth training phantom was the imaging chamber filled with only water. We encourage subsampling these datasets to create limited datasets and comparing the reconstruction results to the ground truth obtainable from the full electrode data.

The actual challenge data is arranged into seven different difficulty levels, labeled 1-7, with each level containing three different phantoms, labeled A-C. As the difficulty level increases, the number of inclusions increases and their shapes become increasingly complex. Furthermore, the measurement data is made more limited by removing measurements collected by some of the electrodes. At each difficulty level above level 1, the data collected from two of the boundary electrodes is removed.

To illustrate a solution to the KTC2023, we have included a simple example reconstruction algorithm, and a Finite Element Method based forward solver used by the reconstruction algorithm. These are provided as both Matlab and Python codes. The reconstructions were computed using linearized difference imaging, and the resulting (EIT) images were then segmented using Otsu's method.

The competitors do not need to follow the above procedure, and are encouraged to explore various image reconstruction and segmentation techniques.

The full data for all the test phantoms will be released after the Kuopio Tomography Challenge 2023 has ended. This data has now been added to this dataset as EvaluationData.zip.

All measurements were conducted at the Process Tomography Laboratory at the University of Eastern Finland.