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bioRxiv · 10.1101/790162

Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images

McKinley, E. T.·Roland, J. T.·Franklin, J. L.·Macedonia, M. C.·Vega, P. N.·Shin, S.·Coffey, R. J.·Lau, K.
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Abstract

Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.

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McKinley, E. T., Roland, J. T., Franklin, J. L., Macedonia, M. C., Vega, P. N., Shin, S., Coffey, R. J., Lau, K.. 2019-10-02. Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images. https://doi.org/10.1101/790162

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