A Deep Learning Pipeline for Cell Segmentation and Viability Quantification in 3D Constructs From Fluorescence Microscopy Images.

With the increasing adoption of 3D cell cultures and bioprinting in biomedical research, there is a growing demand for reliable and accurate monitoring methods. While fluorescence microscopy is widely accepted for 2D cell cultures, when introducing the third dimension it often suffers from signal attenuation and out-of-focus interference, making automated image analysis challenging. In this study, a hybrid pipeline that integrates deep learning and traditional image processing techniques is presented with the aim of establishing an automated tool for cell segmentation and viability assessment in 3D from fluorescence microscopy images. A U2-Net architecture was trained on 2D cell cultures for cell segmentation, while watershed-based separation and intensity-based classification were employed for viability detection. The model demonstrated accurate segmentation with minimal overfitting. Quantitative comparisons with manual counts and ImageJ-based results confirmed its accuracy. We then demonstrated the possibility of extending this model to 3D constructs by tracking cell density and viability during time in the case of human umbilical vein endothelial cells bioprinted in gelatin methacrylate at different seeding densities, taken as case study. Automated counts closely matched manual ones, highlighting the method reliability and minimal invasiveness avoiding the need for sacrificing the construct. Hence, this approach provides a scalable, reproducible, and efficient alternative to manual counting in 3D environments, enabling high-throughput analysis of complex fluorescence microscopy data.