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Electrical impedance tomography data for the challenge

Two datasets were collected for the challenge: one training dataset published with the challenge, and one evaluation dataset which will be made public after the submission deadline of the challenge. The evaluation data for the challenge consists of 21 phantoms, arranged into seven groups of gradually increasing difficulty, with each level containing three different phantoms, labeled A, B, and C. As the difficulty level increases, the number of inclusions increases and their shapes become increasingly complex. Furthermore, the data from some electrodes is discarded as the difficulty level increases, starting with full 32 electrode data at level 1, and reducing by 2 electrodes at each increasing level of difficulty. Each target is assigned to a single difficulty group, therefore, each target is used only once.

The electric current and voltage data is then fed as input to the submitted algorithms for assessment of the reconstructions. See code examples for more details.

The targets have been scanned using all of the measurement electrodes, and have been appropriately subsampled to create the evaluation data. The ground truth images were obtained by segmenting digital photographs of each phantom.

The test dataset will be made public by the end of the competition.

Get the data

The training dataset is available here.

Note: The publicly available data will not be used by the committee for assessing the quality of the algorithms submitted to the challenge. This data is reserved for algorithm development. We have collected a separate test dataset to evaluate algorithm quality.The geometry is the same as in the categories of the public dataset. However, the targets are slightly different in a way that will be made public only after the deadline.


The targets consist of a circular water tank 23 cm in diameter, with inclusions of varying shapes made of either resistive or conductive plastic using a 3D printer, or metallic inclusions. Each target has a different number of irregular inclusions at varying positions.

The dataset collected for the KTC2023 challenge consists of two separate sets, with identical experimental setup and settings. One set is provided to the competitors as training set for algorithm development, and the other will be used by the organizers to test the reconstruction algorithms. The test set will be made public after the end of the competition.

Training dataset

The training set consists of five phantoms with full electrode data. These are designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contain inclusions. A fifth training phantom is made up of water only and contains no inclusions.

We encourage subsampling these datasets to create limited datasets and comparing the reconstruction results to the ground truth obtainable from the full datasets. Training data for each difficulty group can be created by subsampling these datasets to create limited datasets that match the electrodes used in each difficulty level ( See the instructions PDF file ).

Note: As the orientation of EIT reconstructions can depend on the tools used, we have included example reconstructions for each of the training phantoms to demonstrate how the reconstructions obtained from the data and the specified geometry should be oriented. These reconstructions have been computed using a simple example algorithm provided with the training datasets.

We have also included segmentation examples of the reconstructions to demonstrate the desired format for the final competition entries. The segmentation images were obtained by the following steps:

  1. Compute an EIT image reconstruction using the provided simple example algorithm
  2. Determine two threshold levels using triclass Otsu's method on the EIT reconstruction. Divide the image pixels into three classes according to the treshold levels.
  3. Assign the pixels in the largest resulting class with the value 0 (water). Assign the pixels in the other two classes with the value 1 (resistive inclusions) or 2 (conductive inclusions), depending on which class had higher values in the EIT reconstruction.
  4. If the background (water) class is either the bottom/top of the three classes, combine the other two classes and assign their pixels with the value 1 (resistive inclusions) / 2 (conductive inclusions), respectively.

The competitors do not need to follow the above segmentation procedure, and are encouraged to explore various segmentation techniques for the limited-angle reconstructions.

The competitors are encouraged to generate extra training data using simulations. The organizing committee will provide a Matlab and Python finite element code package to simulate EIT data for targets generated by the competitors themselves.

Testing dataset

The test set will be made public after the end of the competition.

Data format

The dataset is shared using the MATLAB .mat files (version 7). Each file contains the measurements for one imaging target.

Python users can load this type of file into their code using the scipy.io.loadmat function found in the module scipy. Please see the provided example codes.

Data collection

The challenge data was measured at the Electrical Tomography Laboratory at the University of Eastern Finland. The measurement device is a current-injection and voltage measurement device designed and constructed in-house. The measurement setup consists of a water tank with 32 equally spaced measurement electrodes, the EIT device, and a digital camera.