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tokens: and using 16 Gigabytes of RAM takes between 5 to 10 s. Including the pixel-error map calculation increases the computing time to approximately 10 min. For comparison, the Super-Pixel method typically requires several minutes to extract the binary field, where the actual time depends on the image pixel resolution and the number of Super-Pixels employed. The computational speed of our network thus makes it particularly useful as a component in a Bayesian statistical inference framework to perform EoR parameter estimation using tomographic statistics (e.g. Gazagnes et al. 2021 ). We compare the accuracy of our approach with a feature finding method from the field of image processing, the so-called Super-Pixel method, which Giri et al. ( 2018 ) showed to be superior over simple thresholding methods. The results show that our neural network can identify neutral regions in the mock observations at least as well and often much better than the Super-Pixel method. We show a visual comparison and the resulting pixel-per-pixel error map tested on our ‘fiducial’ model. This error map gives valuable insight into the parts of the image that are hard to recover and helps us check for overfitting. We studied the accuracy of a range of derived quantities from the recovered binary fields, comparing the performance of SegU-Net with the Super-Pixel method. These quantities are the volume-averaged ionization fraction – the evolution of which provides the reionization history, the size distribution of the ionized (BSD) and neutral (ISD) regions, the dimensionless power spectra of the recovered binary fields, and the three Betti numbers, which quantify the topology of the segmented data sets. For all quantities, we find that the SegU-Net results are more accurate than the Super-Pixel results, especially for the early and late stages of reionization, where the Super-Pixel method often struggles to produce accurate results. Machine learning methods generally are sensitive to the properties of the training set. Therefore, we tested SegU-Net on input data with different properties than the training set. First, we analysed the performance on data sets with different noise levels than the network was trained. We found that SegU-Net yields accurate results for data sets in which the noise level is characterized by an observing time of , which approximately corresponds to an SNR ≳3. Secondly, we applied the network to mock observations calculated from the results of a fully numerical reionization simulation, rather than the seminumerical simulations used to train the network. We find that SegU-Net achieves the same level of accuracy when applied to this data set and therefore is not sensitive to the type of simulation employed during the training process. We want to point out that similar efforts are being made by Gagnon-Hartman et al. ( 2021 ). They focus on reconstructing the segmented maps of ionized and neutral regions in the context of foreground mitigation using the