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bioRxiv · 10.1101/478925

Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms

Lee, J.·Kim, H.·Cho, H.·Jo, Y.·Song, Y.·Ahn, D.·Lee, K.·Park, Y.·Ye, S.-J.
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Abstract

In order to identify cell nuclei, fluorescent proteins or staining agents has been widely used. However, use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis, and even interferes with intrinsic physiological conditions. In this work, we proposed a method of label-free segmentation of cell nuclei in optical diffraction tomography images by exploiting a deep learning framework. The proposed method was applied for precise cell nucleus segmentation in two, three, and four-dimensional label-free imaging. A novel architecture with optimised training strategies was validated through cross-modality and cross-laboratory experiments. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.

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Lee, J., Kim, H., Cho, H., Jo, Y., Song, Y., Ahn, D., Lee, K., Park, Y., Ye, S.-J.. 2018-11-27. Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms. https://doi.org/10.1101/478925

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