Fibrous scaffolds have been extensively used in three-dimensional (3D) cell culture systems to establish in vitro models in cell biology, tissue engineering, and drug screening. It is a common practice to characterize cell behaviors on such scaffolds using confocal laser scanning microscopy (CLSM). As a noninvasive technology, CLSM images can be utilized to describe cell-scaffold interaction under varied morphological features, biomaterial composition, and internal structure. Unfortunately, such information has not been fully translated and delivered to researchers due to the lack of effective cell segmentation methods. We developed herein an end-to-end model called Aligned Disentangled Generative Adversarial Network (AD-GAN) for 3D unsupervised nuclei segmentation of CLSM images. AD-GAN utilizes representation disentanglement to separate content representation (the underlying nuclei spatial structure) from style representation (the rendering of the structure) and align the disentangled content in the latent space. The CLSM images collected from fibrous scaffold-based culturing A549, 3T3, and HeLa cells were utilized for nuclei segmentation study. Compared with existing commercial methods such as Squassh and CellProfiler, our AD-GAN can effectively and efficiently distinguish nuclei with the preserved shape and location information. Building on such information, we can rapidly screen cell-scaffold interaction in terms of adhesion, migration and proliferation, so as to improve scaffold design.
Keywords: 3D nuclei segmentation; Aligned disentangled generative adversarial network; Cell-scaffold interaction; Fibrous scaffold-based cell culture; Unsupervised learning.
Copyright: © 2022 Yao, et al.