肾脏病理学中基础细胞核分割模型的综合评估与微调
Comprehensive Evaluation and Fine-Tuning of Foundational Cell Nuclei Segmentation Models in Renal Pathology
- Vanderbilt University（范德堡大学）
- Weill Cornell Medicine（威尔康奈尔医学院）
- Vanderbilt University Medical Center（范德堡大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过结合易、中、难三种难度标注微调九种细胞核分割模型，发现多难度标注可有效提升模型性能，LSP-DETR达最高F1分数0.8725，StarDist提升最大。
AI中文摘要：
准确的细胞核实例分割对于定量肾脏病理学至关重要，然而通用模型在处理低对比度、密集细胞核、复杂形态和强背景染色时往往表现不佳。在本研究中，我们扩展了一个人在环框架，结合了来自分割良好病例（Easy）的5,901个基础模型生成的伪标签、860个新近由专家标注的未解决挑战性病例（Medium）以及198个专家标注的共识失败病例（Hard）。这些跨越不同分割难度级别的标注，使得我们能够系统评估七种单源和混合源微调策略在九种细胞分割模型配置上的表现。微调改善了所有模型，其中Medium数据包含在九个最佳表现策略中的七个中。LSP-DETR在仅使用Hard微调时达到了最高的F1分数0.8725，而StarDist显示出最大的改进，通过仅使用Medium微调从0.7380提升至0.8332。这些发现表明，跨越多个难度级别的标注支持有效的模型适应，尽管最佳标注组成仍依赖于具体模型。
英文摘要：
Accurate nuclei instance segmentation is essential for quantitative renal pathology, yet general-purpose models often struggle with low contrast, dense nuclei, complex morphology, and strong background staining. In this work, we extended a human-in-the-loop framework by combining 5,901 foundation-model-generated pseudo-labels from well-segmented cases (Easy), 860 newly expert-annotated unresolved challenging cases (Medium), and 198 expert-annotated consensus failure cases (Hard). These annotations, spanning different levels of segmentation difficulty, enabled the systematic evaluation of seven single-source and mixed-source fine-tuning strategies across nine cell segmentation model configurations. Fine-tuning improved all models, with Medium data included in seven of the nine best-performing strategies. LSP-DETR achieved the highest F1 score of 0.8725 with Hard-only fine-tuning, while StarDist showed the largest improvement, increasing from 0.7380 to 0.8332 with Medium-only fine-tuning. These findings show that annotations spanning multiple difficulty levels support effective model adaptation, although the optimal annotation composition remains model dependent.
