Sankaranarayanan Abishek, Khachaturov Georgii, Smythe Kimberly S, Mittal Shachi
Department of Chemical Engineering, University of Washington, Seattle, WA, USA.
Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Commun Biol. 2025 May 30;8(1):836. doi: 10.1038/s42003-025-08184-8.
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.
多重成像技术需要识别组织中的不同细胞类型。为了利用其在细胞和分子分析方面的潜力，在解析大量数据时，特别是在临床环境中，需要高通量且准确的分析方法。细胞核分割错误会在细胞表型分析和单细胞空间分析的所有下游步骤中传播。在此，我们通过评估7种组织类型中约20000个来自人类组织样本的标记细胞核的性能，对多重免疫荧光数据中常用的细胞核分割工具进行基准测试和比较。预训练的深度学习模型优于经典的细胞核分割算法。总体而言，推荐使用Mesmer，因为在我们的综合数据集上，在交并比（IoU）阈值为0.5时，它表现出最高的细胞核分割准确率，F1分数为0.67。如果计算资源有限，推荐使用预训练的StarDist模型，与Mesmer相比，它在使用CPU计算时运行时间提高约12倍，使用GPU计算时提高约4倍，但在密集细胞核区域表现不佳。
