Mesenchymal stem cells (MSCs) are stromal cells which have multi‐lineage differentiation and self‐renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright‐field microscope is time‐consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well‐performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning‐based automated counting process impractically expensive. To address this challenge, self‐supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.

中文翻译：

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利用表征水平对比学习改进明场显微镜图像上的衰老细胞分割

间充质干细胞（MSC）是具有多谱系分化和自我更新潜力的基质细胞。准确估计间充质干细胞中衰老细胞的总数对于临床应用至关重要。使用光学明视野显微镜进行的传统手动细胞计数非常耗时，并且需要专业操作员。在这项研究中，通过深度学习算法对衰老细胞进行自动分割和计数。然而，性能良好的深度学习算法需要大量标记数据集。手动标记非常耗时并且需要专家。这使得基于深度学习的自动计数过程变得不切实际且昂贵。为了应对这一挑战，实施了基于自我监督学习的方法。该方法将表示级对比学习组件合并到实例分割算法中，以利用有限的标记数据进行有效的衰老细胞分割。测试结果表明，与原始分割模型相比，该模型将下游分割任务的平均精度和平均召回率分别提高了8.3%和3.4%。