Abstract
This paper describes an image-processing pipeline for the automatic segmentation of the nuclear envelope of HeLa cells observed through Electron Microscopy. The pipeline was applied to a 3D stack of 300 images. The intermediate results of neighbouring slices are further combined to improve the final results. Comparison with a hand-segmented ground truth reported Jaccard similarity values between 94–98% on the central slices with a decrease towards the edges of the cell where the structure was considerably more complex. The processing is unsupervised and each 2D slice is processed in about 5–10 s running on a MacBook Pro. No systematic attempt to make the code faster was made. These encouraging results could be further used to provide data for more complex segmentation techniques like Deep Learning, which require a considerable amount of data to train architectures like Convolutional Neural Networks. The code is freely available from https://github.com/reyesaldasoro/HeLa-Cell-Segmentation.
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Acknowledgements
This work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001999), the UK Medical Research Council (FC001999), and the Wellcome Trust (FC001999).
The authors acknowledge the support of the Alan Turing Institute through the Data Study Groups organised by Dr Sebastian Vollmer where initial study of this data was made.
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Karabağ, C., Jones, M.L., Peddie, C.J., Weston, A.E., Collinson, L.M., Reyes-Aldasoro, C.C. (2018). Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_23
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