Background:
In organelle imaging, segmentation aims to accurately delineate pixels or voxels corresponding to target organelles from background, noise, and other cellular structures in microscopy images, thereby generating masks suitable for quantitative analysis. Robust segmentation is foundational to downstream quantification, including morphological characterization, spatial distribution analysis, temporal trajectory tracking, and the detection of key biological events. Although super-resolution techniques widely used in live-cell imaging substantially improve spatial resolution, they also introduce challenges such as signal-to-noise variability, phototoxicity constraints, and increased imaging artifacts. Consequently, developing segmentation algorithms that maintain robust performance across diverse microscopy platforms, labeling strategies, and experimental conditions is of critical importance.
Recently, Assoc. Prof. Bo Peng (Northwestern Polytechnical University) and Prof. Lin Li (Xiamen University),
et al. systematically reviewed the evolution of organelle segmentation algorithms in live-cell imaging, highlighting key challenges such as three-dimensional segmentation, simultaneous multi-organelle segmentation, and cross-modality generalization (Figure 1).
Research Progress:
Organelle segmentation methods are broadly grounded in classical image processing and deep learning. Traditional approaches remain effective for high-contrast images with well-defined structures and are commonly used for rapid screening, pseudo-label generation, or post-processing due to their transparency and computational efficiency. In contrast, deep learning models, including FCNs, U-Net, and Mask R-CNN, now dominate complex organelle segmentation. By learning hierarchical features in an end-to-end manner, these methods achieve superior accuracy and robustness for filamentous, branched, and densely overlapping morphologies, enabling automated and high-throughput quantitative analysis across diverse imaging conditions and labeling strategies.
This review adopts a representative organelle–based framework to analyze segmentation challenges driven by morphological heterogeneity and corresponding methodological strategies. Mitochondrial dynamics, characterized by transitions between networked and punctate states with frequent fission and fusion, require integrated workflows combining segmentation, tracking, and event detection. The endoplasmic reticulum’s complex tubular and sheet-like topology demands continuity-preserving segmentation followed by skeletonization and topological analysis. Other organelles, including lysosomes, the Golgi apparatus, and lipid droplets, span scales from puncta to continuous regions, necessitating size-, density-, and label-aware algorithms. Overall, organelle morphology and dynamics fundamentally dictate segmentation strategies, motivating structure-specific algorithm design and evaluation.
The review highlights that advancing from single- to multi-organelle segmentation requires a unified, systems-level framework rather than a mere combination of independent models. Such frameworks enable simultaneous, consistent segmentation of multiple organelles within the same spatial and temporal context while preserving inter-organelle spatial relationships and functional context. This capability establishes a quantitative basis for systematic analysis of organelle interaction networks and coordinated intracellular regulation.
Future Prospects:
This work systematically reviews key challenges in the field, including cross-modality generalization, the computational burden of three-dimensional data, and heavy reliance on annotated datasets. To address these issues, it highlights strategies such as self-supervised and transfer learning to reduce annotation demands, the use of synthetic data and physics-informed constraints to enhance robustness, small-sample and active learning to improve labeling efficiency, and fine-tuning frameworks based on general-purpose segmentation foundation models to promote standardization. Together, these advances are poised to transform organelle segmentation from a supporting research tool into a scalable quantitative infrastructure, enabling a paradigm shift in cell biology from qualitative observation to quantitative analysis.
The complete study is accessible via DOI:10.34133/research.1035