新AI模型大幅提升肾病理细胞分割准确率，尤其解决旧模型难处理的复杂病例。
Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models
- 采用人类参与评分框架，对比2025年新旧细胞基础模型性能。
- 最新融合模型在2091个难题样本中达62.2%优秀评级，错误率仅0.4%。
- 提供高质量挑战样本数据集，助力未来肾病专用模型优化。
准确的细胞核分割对肾病理下游任务至关重要，但因肾组织形态多样性和成像差异仍具挑战。尽管此前已评估早期生成的AI细胞基础模型，但近期模型的有效性尚不明确。本研究基于大规模肾图像块数据集，在人机协同评分框架下，对比2025年先进模型（包括CellViT++变体和Cellpose-SAM）与2024年前开发的三大主流模型。通过融合集成与模型一致性分析评估分割能力。结果表明，CellViT++ [Virchow] 在独立运行中表现最佳，2091个精选难题样本中有40.3%被评为“良好”，优于所有旧模型。融合模型进一步实现62.2%“良好”预测，错误率仅为0.4%，显著降低分割误差。值得注意的是，该融合模型（2025）成功解决了前次研究中未被处理的多数难题案例。研究证实了AI细胞基础模型在肾病理中的潜力，并提供了可用于未来肾病专用模型优化的精选挑战样本数据集。
原文摘要 · Abstract (English)
Accurate cell nuclei segmentation is critical for downstream tasks in kidney pathology and remains a major challenge due to the morphological diversity and imaging variability of renal tissues. While our prior work has evaluated early-generation AI cell foundation models in this domain, the effectiveness of recent cell foundation models remains unclear. In this study, we benchmark advanced AI cell foundation models (2025), including CellViT++ variants and Cellpose-SAM, against three widely used cell foundation models developed prior to 2024, using a diverse large-scale set of kidney image patches within a human-in-the-loop rating framework. We further performed fusion-based ensemble evaluation and model agreement analysis to assess the segmentation capabilities of the different models. Our results show that CellViT++ [Virchow] yields the highest standalone performance with 40.3% of predictions rated as "Good" on a curated set of 2,091 challenging samples, outperforming all prior models. In addition, our fused model achieves 62.2% "Good" predictions and only 0.4% "Bad", substantially reducing segmentation errors. Notably, the fusion model (2025) successfully resolved the majority of challenging cases that remained unaddressed in our previous study. These findings demonstrate the potential of AI cell foundation model development in renal pathology and provide a curated dataset of challenging samples to support future kidney-specific model refinement.
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