HeLa cell images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.

This is a data set that contains <strong>labeled HeLa cell images</strong>, generated in MATLAB® Image Labeler, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background for <strong>deep learning</strong> architecture training. Details of the imaging and preparation have been published in: Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> 2019, <em>5</em>(9), 75; https://doi.org/10.3390/jimaging5090075 and The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR. Details on the segmentation and analysis of HeLa cells have been published in: Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures, BioRxiv, https://doi.org/10.1101/2020.03.05.978478 and Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> 2019, <em>5</em>(9), 75; https://doi.org/10.3390/jimaging5090075