ArticleCommunications biology2025
Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- Precision in 3D: A Fast and Accurate Algorithm for Reproducible Motoneuron Structure and Protein Expression Analysis.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
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Abstract
Multiplexed imaging techniques require identifying different cell types in the tissue. To utilize their potential for cellular and molecular analysis, high throughput and accurate analytical approaches are needed in parsing vast amounts of data, particularly in clinical settings. Nuclear segmentation errors propagate in all downstream steps of cell phenotyping and single-cell spatial analyses. Here, we benchmark and compare the nuclear segmentation tools commonly used in multiplexed immunofluorescence data by evaluating their performance across 7 tissue types encompassing ~20,000 labeled nuclei from human tissue samples. Pre-trained deep learning models outperform classical nuclear segmentation algorithms. Overall, Mesmer is recommended as it exhibits the highest nuclear segmentation accuracy with 0.67 F1-score at an IoU threshold of 0.5 on our composite dataset. Pre-trained StarDist model is recommended in case of limited computational resources, providing ~12x run time improvement with CPU compute and ~4x improvement with the GPU compute over Mesmer, but it struggles in dense nuclear regions.
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