Kohtala Sampsa, Nedal Tonje Marie Vikene, Kriesi Carlo, Moen Siv Helen, Ma Qianli, Ødegaard Kristin Sirnes, Standal Therese, Steinert Martin
TrollLABS, Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Front Cell Dev Biol. 2022 Jul 5;10:941542. doi: 10.3389/fcell.2022.941542. eCollection 2022.
A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.
平衡的骨骼重塑过程对于保持健康至关重要。可以通过分析从外周血或血沉棕黄层中分离出的单核细胞分化而来的破骨细胞来研究重塑过程。破骨细胞是高度特化的多核细胞，可分解骨组织。在培养物中识别并正确定量破骨细胞通常由训练有素的人员使用光学显微镜进行，这既耗时又容易受到操作者偏差的影响。我们使用机器学习方法，对来自7名人类外周血单核细胞（PBMC）供体的307张不同的孔板图像进行分析，这些图像中总共包含94,974个标记的破骨细胞，从而提出了一种从显微图像中定量人类破骨细胞的高效且可靠的方法。我们使用了一个名为Darknet（YOLOv4）的基于深度学习的开源目标检测框架来训练和测试多个模型，以分析所提出方法的适用性和通用性。在一个独立测试集上，训练后的模型平均精度达到85.26%，与人类注释者的相关系数为0.99，并且每种培养物中平均比人类多计数2.1%的破骨细胞。此外，训练后的模型比两名独立的人类注释者之间的一致性更高，这支持了一种更可靠、偏差更小的破骨细胞定量方法，同时节省了时间和资源。我们邀请感兴趣的研究人员在我们的模型上测试他们的数据集，以进一步加强和验证结果。
