The purpose of segmentation in organelle imaging is to accurately delineate pixels or voxels corresponding to target organelles from background, noise, and other cellular structures in microscopic images, thereby generating masks suitable for quantitative analysis. Robust segmentation is the basis for downstream quantification such as morphological characterization, spatial distribution analysis, temporal trajectory tracking, and detection of key biological events.
Super-resolution techniques, widely used in live cell imaging, greatly improve spatial resolution but also pose challenges such as signal-to-noise fluctuations, phototoxicity limitations, and increased imaging artifacts. Therefore, it is critical to develop segmentation algorithms that maintain robust performance across different microscopy platforms, labeling strategies, and experimental conditions.
Recently, Assoc. Prof. Bo Peng (Northwestern Polytechnical University) and Prof. Lin Li (Xiamen University), Others. We systematically review the evolution of organelle segmentation algorithms in live cell imaging, highlighting key challenges such as three-dimensional segmentation, simultaneous multiple organelle segmentation, and cross-modality generalization (Figure 1).
Research progress
Organelle segmentation methods are broadly based on 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 such as FCN, U-Net, and Mask R-CNN currently dominate complex organelle segmentation. By learning hierarchical features end-to-end, these methods achieve excellent accuracy and robustness for filamentous, branched, and tightly overlapping morphologies, enabling automated, high-throughput quantitative analysis across a variety of imaging conditions and labeling strategies.
In this review, we employ a representative organelle-based framework to analyze the segmentation challenges posed by morphological heterogeneity and corresponding methodological strategies. Mitochondrial dynamics are characterized by transitions between network and point-like states with frequent fission and fusion, requiring integrated workflows that combine segmentation, tracking, and event detection. The complex tubular and sheet-like topologies of the endoplasmic reticulum require continuity-preserving segmentation followed by skeletonization and topological analysis.
Other organelles, such as lysosomes, the Golgi apparatus, and lipid droplets, extend from the punctum to a contiguous region and require size, density, and label-aware algorithms. Overall, organelle morphology and dynamics fundamentally determine segmentation strategies and motivate structure-specific algorithm design and evaluation.
This review highlights that advancing from single organelle segmentation to multi-organelle segmentation requires an integrated system-level framework, rather than just a combination of independent models. Such a framework allows for simultaneous and consistent segmentation of multiple organelles within the same spatial and temporal context while preserving the spatial relationships and functional context between organelles. This feature establishes a quantitative basis for systematic analysis of organelle interaction networks and coordinated intracellular regulation.
This study systematically reviews the major challenges in this field, including cross-modality generalization, computational burden of 3D data, and heavy reliance on annotated datasets. To address these issues, we emphasize strategies such as self-supervised transfer learning to reduce annotation demands, the use of constraints based on synthetic data and physical information to increase robustness, small samples and active learning to improve labeling efficiency, and fine-tuning of frameworks based on generic segmentation foundation models to promote standardization.
These advances transform organelle segmentation from an ancillary research tool to a scalable quantitative infrastructure, enabling a paradigm shift in cell biology from qualitative observation to quantitative analysis.
sauce:
Science and Technology Review Publishing
Reference magazines:
Dirty. , Others. (2026) Artificial intelligence for organelle segmentation in live cell imaging. Journal of Dairy Science. DOI: 10.34133/research.1035. https://spj.science.org/doi/10.34133/research.1035.
