Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status
Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status
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DOI：
10.1016/j.compbiomed.2020.104172
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发表时间：
2021-02-01
影响因子：
7.7
通讯作者：
Jonas，Stephan
中科院分区：
文献类型：
--
作者：
Piotrowski，Tobias;Rippel，Oliver;Jonas，Stephan
人诱导多能干细胞（hiPSC）能够分化成多种人组织细胞。它们为个性化医疗和药物筛选提供了新的机会。这需要大量高质量的hiPSC，只能通过自动化培养获得。自动化培养的主要要求之一是对细胞状况进行定期的非侵入性分析，例如通过全孔显微镜。然而，尽管这一要求很紧迫，但目前还没有基于图像处理的自动解决方案来进行这种性质的多类别常规量化。本文描述了一种基于相差显微镜和深度学习的全自动细胞状态识别方法。该方法可用于自动化hiPSC培养期间的过程中控制。基于U-Net的算法能够分割hiPSC集落形成的重要参数，并且可以区分hiPSC集落、单细胞、分化细胞和死细胞的类别。对于hiPSC集落、分化细胞、单个hiPSC和死细胞类别，该模型实现了比熟练专家的视觉估计更准确的结果。此外，每个hiPSC集落的参数直接来源于分类结果，例如圆度、大小、重心和其他细胞的内含物。这些参数提供了关于细胞状态的局部信息，并能够在自动化过程中对细胞培养物进行良好的处理。因此，该模型可用于自动化hiPSC培养期间的常规非侵入性图像分析。这有助于产生用于生物医学目的的高质量hiPSC衍生产品。
Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.