Cell Identification from Microscopy Images using Deep Learning on Automatically Labeled Data

Abstract: In biology, cell counting provides a fundamental metric for live-cell experiments. Unfortunately, most researchers are constrained to using tedious and invasive methods for counting cells. Automatic identification of cells in microscopy images would therefore be a valuable tool for such researchers. In recent years, deep learning-based image segmentation methods such as the U-Net have been explored for this task. However, deep learning models often require large amounts of labeled data for training. For identifying cells in microscopy images, this type of labeled data is commonly generated through manual pixel-wise annotations of hundreds of cells. To address this problem, we explore an approach for automatically generating large numbers of labeled examples by imaging cells that were stained with a fluorescent dye. By using fluorescence microscopy alongside non-invasive microscopy, we obtain visualizations of the positions of nuclei in each cell image. We transform the fluorescence images into binary masks with a pipeline based on classical segmentation techniques: histogram equalization through CLAHE and thresholding using Otsu's method. We then use these masks as labels for the cell images, so that each image is accompanied by pixel-wise annotations of the nuclei. We generate datasets for three different cell types, and use them to train U-Net models for automatic cell identification. The trained models show excellent performance (~2% false positives, <1% false negatives), on par with expert annotation. This method therefore shows great promise as a tool for biologists to perform automatic cell identification and counting. The trained U-Nets can potentially also be used for tracking cells in time-lapse imaging. These new data extraction methods could assist researchers in deepening their understanding of the phenomena that they are studying.