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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Analyzing Cell-Scaffold Interaction through Unsupervised 3D Nuclei Segmentation.

Kai Yao^1,2, Jie Sun¹, Kaizhu Huang¹

¹School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, Jiangsu 215123, China.

International Journal of Bioprinting

|February 21, 2022

Summary

We developed an Aligned Disentangled Generative Adversarial Network (AD-GAN) for 3D unsupervised nuclei segmentation in confocal laser scanning microscopy images. This method accurately identifies cell nuclei, improving the analysis of cell-scaffold interactions for better biomaterial design.

Failed At:

2026-07-14T07:27:38.939119+00:00

Keywords:

3D nuclei segmentation Aligned disentangled generative adversarial network Cell-scaffold interaction Fibrous scaffold-based cell culture Unsupervised learning

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