Journal
EXPERIMENTAL CELL RESEARCH
Volume 433, Issue 2, Pages -Publisher
ELSEVIER INC
DOI: 10.1016/j.yexcr.2023.113807
Keywords
Transmission light microscopy; Cell organelle dynamics; 3DCNAS neural network; Neural architecture search
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This study proposes a method called 3DCNAS to predict subcellular structures of fluorescence using unlabeled transmitted light microscope images. The method leverages deep learning techniques to analyze cell images and extract meaningful information.
Cellular biology research relies on microscopic imaging techniques for studying the complex structures and dynamic processes within cells. Fluorescence microscopy provides high sensitivity and subcellular resolution but has limitations such as photobleaching and sample preparation challenges. Transmission light microscopy offers a label-free alternative but lacks contrast for detailed interpretation. Deep learning methods have shown promise in analyzing cell images and extracting meaningful information. However, accurately learning and simulating diverse subcellular structures remain challenging. In this study, we propose a method named three-dimensional cell neural architecture search (3DCNAS) to predict subcellular structures of fluorescence using unlabeled transmitted light microscope images. By leveraging the automated search capability of differentiable neural architecture search (NAS), our method partially mitigates the issues of overfitting and underfitting caused by the distinct details of various subcellular structures. Furthermore, we apply our method to analyze cell dynamics in genome-edited human induced pluripotent stem cells during mitotic events. This allows us to study the functional roles of organelles and their involvement in cellular processes, contributing to a comprehensive understanding of cell biology and offering insights into disease pathogenesis.
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