Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies
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 se...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-08184-8 |
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| author | Abishek Sankaranarayanan Georgii Khachaturov Kimberly S. Smythe Shachi Mittal |
| author_facet | Abishek Sankaranarayanan Georgii Khachaturov Kimberly S. Smythe Shachi Mittal |
| author_sort | Abishek Sankaranarayanan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0bd40dff19c944e6b6f11adaa3e02c04 |
| institution | DOAJ |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-0bd40dff19c944e6b6f11adaa3e02c042025-08-20T03:16:47ZengNature PortfolioCommunications Biology2399-36422025-05-018111410.1038/s42003-025-08184-8Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studiesAbishek Sankaranarayanan0Georgii Khachaturov1Kimberly S. Smythe2Shachi Mittal3Department of Chemical Engineering, University of WashingtonDepartment of Chemical Engineering, University of WashingtonTranslational Science and Therapeutics Division, Fred Hutchinson Cancer CenterDepartment of Chemical Engineering, University of WashingtonAbstract 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.https://doi.org/10.1038/s42003-025-08184-8 |
| spellingShingle | Abishek Sankaranarayanan Georgii Khachaturov Kimberly S. Smythe Shachi Mittal Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies Communications Biology |
| title | Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| title_full | Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| title_fullStr | Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| title_full_unstemmed | Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| title_short | Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| title_sort | quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies |
| url | https://doi.org/10.1038/s42003-025-08184-8 |
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