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Background <p>Large-scale artificial intelligence foundation models have emerged as promising tools for addressing healthcare challenges, including digital pathology. While many have been developed for complex tasks such as disease diagnosis and tissue quantification using extensive and diverse datasets, their readiness for seemingly simpler tasks, such as nuclei segmentation within a single organ (for example, the kidney), remains unclear. This study answers two questions: How good are current cell foundation models? and How can we improve them?</p> Methods <p>We curated a multi-center, multi-disease, and multi-species dataset sampled from 2542 kidney whole slide images. Three state-of-the-art cell foundation models—Cellpose, StarDist, and CellViT—were evaluated. To enhance performance, we developed a human-in-the-loop strategy that distilled multi-model predictions, improving data quality while reducing reliance on pixel-level annotation. Fine-tuning was performed using the enriched datasets, and segmentation performance was quantitatively assessed.</p> Results <p>Here we show that cell nuclei segmentation in kidney pathology still requires improvement with more organ-targeted foundation models. Among the evaluated models, CellViT achieves the highest baseline performance, with an F1 score of 0.78. Fine-tuning with enriched data improves all three models, with StarDist achieving the highest F1 score of 0.82. The combination of the foundation model-generated pseudo-labels and a subset of pathologist-corrected “hard” patches yields consistent performance gains across all models.</p> Conclusions <p>This study establishes a benchmark for the development and deployment of cell AI foundation models tailored to real-world data. The proposed framework, which leverages foundation models with reduced expert annotation, supports more efficient workflows in clinical pathology.</p>

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Evaluating cell AI foundation models in kidney pathology with human-in-the-loop enrichment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

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  • Junlin Guo,
  • Siqi Lu,
  • Can Cui,
  • Ruining Deng,
  • Tianyuan Yao,
  • Zhewen Tao,
  • Yizhe Lin,
  • Marilyn Lionts,
  • Quan Liu,
  • Juming Xiong,
  • Yu Wang,
  • Shilin Zhao,
  • Catie Chang,
  • Mitchell Wilkes,
  • Agnes Fogo,
  • Mengmeng Yin,
  • Haichun Yang,
  • Yuankai Huo

摘要

Background

Large-scale artificial intelligence foundation models have emerged as promising tools for addressing healthcare challenges, including digital pathology. While many have been developed for complex tasks such as disease diagnosis and tissue quantification using extensive and diverse datasets, their readiness for seemingly simpler tasks, such as nuclei segmentation within a single organ (for example, the kidney), remains unclear. This study answers two questions: How good are current cell foundation models? and How can we improve them?

Methods

We curated a multi-center, multi-disease, and multi-species dataset sampled from 2542 kidney whole slide images. Three state-of-the-art cell foundation models—Cellpose, StarDist, and CellViT—were evaluated. To enhance performance, we developed a human-in-the-loop strategy that distilled multi-model predictions, improving data quality while reducing reliance on pixel-level annotation. Fine-tuning was performed using the enriched datasets, and segmentation performance was quantitatively assessed.

Results

Here we show that cell nuclei segmentation in kidney pathology still requires improvement with more organ-targeted foundation models. Among the evaluated models, CellViT achieves the highest baseline performance, with an F1 score of 0.78. Fine-tuning with enriched data improves all three models, with StarDist achieving the highest F1 score of 0.82. The combination of the foundation model-generated pseudo-labels and a subset of pathologist-corrected “hard” patches yields consistent performance gains across all models.

Conclusions

This study establishes a benchmark for the development and deployment of cell AI foundation models tailored to real-world data. The proposed framework, which leverages foundation models with reduced expert annotation, supports more efficient workflows in clinical pathology.