Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Drug Applied Research Center, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Comput Biol Med. 2019 Sep;112:103365. doi: 10.1016/j.compbiomed.2019.103365. Epub 2019 Jul 22.
Stem cells are a group of competent cells capable of self-renewal and differentiating into osteogenic, chondrogenic, and adipogenic lineages. These cells provide the possibility of successfully treating patients. During differentiation into adipose tissues, a large number of lipid droplets normally accumulate in these cells, which can be seen through oil red O staining. Although the oil red O staining technique is regularly used for assessing the differentiation degree, its validity for quantitative studies has not been approved yet. Lipid droplet counting has applications in differentiation works and saves time and costs once being automated. In this research, for proving the differentiation of mesenchymal stem cells (MSCs) into adipocyte tissues, their microscopic images were provided. Then, the microscopic images were segmented into square patches, and the lipid droplets were annotated through single-point annotation. The proposed network, based on deep learning, is a fully convolutional regression network processing an image with a small respective field on it. Finally, this method not only does count the lipid droplets but also generates a count map. The average counting accuracy is 94%, which is higher than that of the state-of-the-art methods. It is useful to cell biologists to check the percentage of differentiation in different samples. Also, with a count map, it is possible to observe the regions with high concentrations of lipid droplets without oil red O staining and, thus, examine the total adipocyte differentiation. The contribution of this paper is that a deep learning algorithm has been used for the first time in the field of processing intracellular images.
干细胞是一群具有自我更新能力并能分化为成骨细胞、软骨细胞和成脂细胞谱系的细胞。这些细胞为成功治疗患者提供了可能。在分化为脂肪组织的过程中，这些细胞中通常会积累大量的脂滴，可以通过油红 O 染色观察到。尽管油红 O 染色技术常用于评估分化程度，但尚未证实其在定量研究中的有效性。脂滴计数在分化工作中有应用，一旦实现自动化，就可以节省时间和成本。在这项研究中，为了证明间充质干细胞（MSCs）向脂肪细胞组织的分化，提供了它们的显微镜图像。然后，将显微镜图像分割成正方形补丁，并通过单点注释对脂滴进行注释。基于深度学习的提出的网络是一个全卷积回归网络，处理其相应字段较小的图像。最后，该方法不仅可以计数脂滴，还可以生成计数图。平均计数准确率为 94%，高于现有方法的准确率。它对细胞生物学家检查不同样本中的分化百分比很有用。此外，通过计数图，可以观察到没有油红 O 染色的高浓度脂滴区域，从而检查总脂肪细胞分化。本文的贡献在于首次将深度学习算法应用于处理细胞内图像的领域。
