Thayer School of Engineering, Dartmouth College, Hanover, 03755, USA.
Geisel School of Medicine, Dartmouth College, Hanover, 03755, USA.
Sci Rep. 2024 Nov 6;14(1):27016. doi: 10.1038/s41598-024-77943-0.
Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification by incorporating synthetic data. This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.
我们的研究开发了一种基于生成对抗网络（GAN）的方法，该方法可以生成人类心肌细胞在成熟过程中不同阶段的逼真的合成图像数据，作为一种工具，可以显著提高细胞的分类准确性，并最终有助于细胞结构和功能计算分析的通量。人类诱导多能干细胞衍生的心肌细胞（hiPSC-CMs）在具有生理硬度的微图案化胶原涂层水凝胶上培养，以促进成熟，并进行光学测量以进行结构和功能分析。对照组在涂有胶原的玻璃培养皿上培养。这些图像记录被用作真实数据来训练 GAN 模型。结果表明，GAN 方法能够复制真实数据的真实特征，并且与仅使用通常在规模和多样性上受限的真实实验数据相比，包含此类合成数据可以显著提高分类准确性。与仅使用真实实验数据相比，该模型通过整合合成数据，在数据分类和数据泛化能力方面优于四种传统机器学习算法。这项工作证明了在样本量有限的情况下整合合成数据的重要性，从而有效地解决了数据可用性带来的挑战。
