Label-free three-dimensional analyses of live cells with deep-learning-based segmentation exploiting refractive index distributions

Abstract

Visualisations and analyses of cellular and subcellular organelles in biological cells is crucial for the study of cell biology. However, existing imaging methods require the use of exogenous labelling agents, which prevents the long-time assessments of live cells in their native states. Here we propose and experimentally demonstrate three-dimensional segmentation of subcellular organelles in unlabelled live cells, exploiting a 3D U-Net-based architecture. We present the high-precision three-dimensional segmentation of cell membrane, nucleus membrane, nucleoli, and lipid droplets of various cell types. Time-lapse analyses of dynamics of activated immune cells are also analysed using label-free segmentation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/445351v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@b92093org.highwire.dtl.DTLVardef@9caa8org.highwire.dtl.DTLVardef@d81408org.highwire.dtl.DTLVardef@b3c87_HPS_FORMAT_FIGEXP M_FIG C_FIG