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High-throughput imaging techniques have catalyzed significant strides in regenerative medicine, predominantly through advancements in stem cell research. Despite this, the analysis of these images often overlooks important biological implications due to the persistent challenge posed by artifacts during segmentation. In addressing this challenge, this study introduces a new deep learning architecture: a cross-structure, artifact-free U-Net (AFU-Net) model designed to optimize in vitro virtual nuclei staining of stem cells. This innovative framework, inspired by U-Net-based models, incorporates a cross-structure noise removal pre-processing layer. This layer has shown proficiency in handling artifacts frequently found on the peripheries of bright-field images used in stem cell manufacturing processes. In our extensive analysis using a gradient-density dataset of mesenchymal stem cell images, our model consistently outperformed established models in the domain. Specifically, when assessed using critical segmentation evaluation metrics— Segmentation Covering (SC) and Variation of Information (VI)—the proposed model yielded impressive results. It achieved a mean SC of 0.979 and a mean VI of 0.194, standing out from other standard configurations. Further optimization was evident in scenarios involving overlapping tiling, where the model was tasked with countering artifacts from segmented cells. Here, within a cell media setting, the model reached an elevated mean SC of 0.980 and a reduced mean VI of 0.187. The outcomes from our investigations signify a marked enhancement in the standardization and efficiency of stem cell image analysis. This facilitates a more nuanced understanding of cellular analytics derived from label-free images, bridging crucial gaps in both research and clinical applications of stem cell methodologies. While the primary focus has been on stem cells, the potential applicability of our architecture holds promise for broader realms, encompassing various biological and medical imaging contexts.
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