Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images

Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images. We also propose a U-net structure to extract extraembryonic regions on a micropattern, and an algorithm to compares intensities of the fluorescence images staining respective germ-layer cells and extract their regions. The proposed method thus can quantify the pluripotency of a hiPSC line with spatial patterning including cell numbers, areas and distributions of germ-layer and extraembryonic cells on a micropattern, and reveal the formation process of hiPSCs and germ layers in the early differentiation stage by segmenting live-cell bright-field images. In our assay, the cell-number accuracy achieved 86% and 85%, and the cell region accuracy 89% and 81% for segmenting Hoechst and bright-field micropattern images, respectively. Applications to micropattern images of multiple hiPSC lines, micropattern sizes, groups of markers, living and fixed cells show the proposed method can be expected to be a useful protocol and tool to quantify pluripotency of a new hiPSC line before providing it to the scientific community.

Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images

Accurate single cell segmentation provides means to monitor the behavior of single cell within a population of cells. Time-lapse fluorescence images are used to reveal heterogeneous nature of single mouse embryonic stem cell (ESC) colony and monitor fluctuations of the cell states. Automatic quantification of speed and status shifts of the ESCs depends on accurate single cell segmentation that is used to calculate the 3D center of every cell and track this cell for the quantification. This study proposes a new 3D U-net to accurately detect center of each single cell in 3D confocal images. The dimension of input 3D images to the U-net is flexible so that multiple center detections from different image directions can be implemented simultaneously to improve the center detection accuracy. This study showed that our method can improve accuracy for cell center detection and thus the quantification for ESC speeds and status shifts.

U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images

Cell segmentation at a single cell resolution is required to provide insights for basic biology and application study. However, there are issues of low signal-to-noise ratio, weak fluorescence response, and insufficient resolution along the image stacking direction in 3D confocal images (volume). It has been difficult to segment out single cells from close or contacted cells in a cell volume using image processing methods or together with geometric processing methods. Recently, 3D deep learning methods have been used to avoid tedious parameter settings in the image and geometric processing, but still not easy to segment out close or contacted single cells. This paper proposes a 2D U-net to segment cell regions in high accuracy and computing performance. Better 3D cell images and single cell segmentation for close or contacted cells are achieved by combining a 3D U-net to detect the centers of single cells in the volume.

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers. Then, cell correspondences in the correspondent colonies are computed using the recorded cell centers. The examples show the proposed cell tracking method can achieve high tracking accuracy for time-lapse 3D images of undifferentiated but self-renewing mouse embryonic stem (mES) cells where the number and mobility of ES cells in a cell colony may change suddenly by a colony merging or splitting, and cell proliferation or death. The geometric data in the hierarchical data structures also help the visualization and quantitation of the cell shapes and mobility.

High Resolution U-Net for Quantitatively Analyzing Early Spatial Patterning of Human Induced Pluripotent Stem Cells on Micropatterns

Human induced pluripotent stem cells (hiPSCs) can differentiate into three germ layer cells, i.e. ectoderm, mesoderm and endoderm, on micropatterned chips in highly synchronous and reproducible manners. The cells are confined within the chip, expanding two-dimensionally as almost in the form of monolayer, thus to be ideal for serving quantitative analysis of their pluripotency. We present a new U-Net (MP-UNet) structure for cell segmentation of early spatial patterning of hiPSCs on micropattern chips using Hoechst fluorescence images. In this structure, the encoding/decoding layers can be dynamically adjusted to extract sufficient image features and be flexible to image sizes. Dice and weight loss functions are designed to identify slight difference in low signal-to-noise ratio, high boundary-to-area ratio and compacted cell images. Several sizes of Hoechst images were tested to show MP-UNet can achieve high accuracy in cell regions and number counting for various sizes of micropattern chips, thus to be excellent quantitative tool for early spatial patterning of hiPSCs.