Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms

Abstract

We proposed a method of label-free segmentation of cell nuclei by exploiting a deep learning (DL) framework. Over the years, fluorescent proteins and staining agents have been widely used to identify cell nuclei. However, the use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis and even interferes with intrinsic physiological conditions. Without any agents, the proposed method was applied to label-free optical diffraction tomography (ODT) of human breast cancer cells. A novel architecture with optimized training strategies was validated through cross-modality and cross-laboratory experiments. The nucleus volumes from the DL-based label-free ODT segmentation accurately agreed with those from fluorescent-based. Furthermore, the 4D cell nucleus segmentation was successfully performed for the time-lapse ODT images. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.INDEX TERMS Cell nucleus segmentation, deep learning, label-free segmentation, optical diffraction tomography, refractive index tomogram.

Optical quantification of intracellular mass density and cell mechanics in 3D mechanical confinement

“…This is different from our 2D results as well as compared to recent 2D data on RI distribution across various cell lines including MDA-MB-231, where it was shown that cells cytoplasm have higher RI than their nuclei using a wide variety of microscopy techniques. [23][24][25][26][27][28]59,60 Such discrepancy might be attributed to differences in cytoskeletal and/or nuclear morphologies in 2D compared to 3D. Cells on 2D substrates spread out and elongate, displaying a forced ventral-dorsal polarity compared to the non-polarized shape of cells in 3D.…”

Three-dimensional tomography of red blood cells using deep learning

“…Recently, deep neural networks (DNNs) have been successful in various optical applications, such as enhancement of the transverse resolution, 9 phase retrieval from intensity measurements, 10,11 digital staining, 12,13 classification/segmentation based on holographic/tomographic measurements, [14][15][16] and others. 10,17,18 There are some previous demonstrations of the benefits of applying DNNs to the reconstruction of RI values in ODT.…”

Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

“…Recent advances in artificial intelligence (AI) have suggested unexplored domains of QPI beyond simply characterizing biological samples. As datasets obtained from QPI do not rely on the variability of staining quality, various machine learning and deep learning approaches can exploit uniform-quality and high-dimensional datasets to perform label-free image classification (Chen et al 2016;Jo et al 2017;Nissim et al 2020;Ozaki et al 2019;Wang et al 2020;Wu et al 2020;Yoon et al 2017;Zhang et al 2020;Zhou et al 2020) and inference (Chang et al 2020;Choi et al 2019;Dardikman-Yoffe et al 2020;Kandel et al 2020;Lee et al 2019;Nguyen et al 2018;Nygate et al 2020;Pitkäaho et al 2019;Rivenson et al 2018). Such synergetic approaches for label-free blood cell identification have also been demonstrated, which are of interest to this work (Go et al 2018;Kim et al 2019;Nassar et al 2019;Ozaki et al 2019;Singh et al 2020;Yoon et al 2017).…”