A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids

• Published in: Biology methods and protocols Vol. 10; no. 1; p. bpaf030
• Main Authors: Mali, Ajay K, Murugappan, Sivasubramanian, Prasad, Jayashree Rajesh, Tofail, Syed A M, Thorat, Nanasaheb D
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2025
• Subjects: Cancer; Cell viability; Deep learning; Image processing; Medical research; Morphology; Physical characteristics; Segmentation; Spheroids; Toxicity; Tumor microenvironment; spheroid viability; toxicology; drug discovery; U-Net; convolutional neural network; high-throughput screening; live/dead assay; artificial intelligence
• ISSN: 2396-8923; 2396-8923
• DOI: 10.1093/biomethods/bpaf030

Three-dimensional (3D) spheroid models have advanced cancer research by better mimicking the tumour microenvironment compared to traditional two-dimensional cell cultures. However, challenges persist in high-throughput analysis of morphological characteristics and cell viability, as traditional methods like manual fluorescence analysis are labour-intensive and inconsistent. Existing AI-based approaches often address segmentation or classification in isolation, lacking an integrated workflow. We propose a scalable, two-stage deep learning pipeline to address these gaps: (i) a U-Net model for precise detection and segmentation of 3D spheroids from microscopic images, achieving 95% prediction accuracy, and (ii) a CNN Regression Hybrid method for estimating live/dead cell percentages and classifying spheroids, with an R2value of 98%. This end-to-end pipeline automates cell viability quantification and generates key morphological parameters for spheroid growth kinetics. By integrating segmentation and analysis, our method addresses environmental variability and morphological characterization challenges, offering a robust tool for drug discovery, toxicity screening, and clinical research. This approach significantly improves efficiency and scalability of 3D spheroid evaluations, paving the way for advancements in cancer therapeutics.