The increased adoption of three-dimensional (3D) cultures in high-content screening has created an analytical bottleneck in image analysis, prompting the development of automated image analysis tools. This review examines the evolution of automated tools for analysing brightfield and phase-contrast images of spheroids and organoids, from semi-automated approaches through classical machine learning pipelines to modern deep learning tools. Each generation is evaluated according to its underlying principles and limitations, especially when applied to heterogeneous datasets. Rule-based tools were introduced to automate the measurement of morphometric parameters established in manual workflows but may not generalise when experimental conditions change. Classical machine learning improved robustness to post-treatment morphologies but requires retraining when imaging conditions change. A range of deep learning architectures that learn directly from datasets has been introduced, partially reducing the retraining burden associated with classical machine learning. No generation supersedes another across all experimental contexts, and tool selection should be guided by the biological endpoint, image conditions, culture morphology, annotation availability and computational resources. Most tools analyse two-dimensional projections of inherently 3D structures, reducing the accuracy of volume estimates for irregular structures. Independent benchmarks demonstrate that tool performance varies across datasets and imaging laboratories. Future tool development should prioritise robustness across imaging conditions, identification of segmentation failure modes and validation on shared datasets.
Three-dimensional (3D) spheroid and microtissue models are increasingly used in phenotypic drug discovery, yet their scalability for high-throughput screening often remains limited, constrained by the cost and complexity of conventional imaging-based readouts. Here, we present a morphology-based screening approach using low-resolution brightfield images of human retinal pigment epithelium microtissues (hRPE-MTs) to identify fibrotic and non-fibrotic phenotypes.The results of this study demonstrate that extracting geometric, intensity, and texture features from segmented spheroids enables quantitative characterization of fibrosis-associated morphological changes. Machine learning classifiers trained on these features effectively discriminated between fibrotic and non-fibrotic hRPE-MTs.Collectively, these findings indicate that label-free brightfield morphology encodes sufficient information to support scalable phenotypic screening in 3D fibrosis models. This approach provides a low-complexity and broadly accessible strategy for early-stage drug discovery.
