Z-stack扫描提升AI识别脑膜瘤有丝分裂能力，敏感度提高17.14%。
Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas
- 使用多焦层扫描获取深度信息，增强图像细节。
- 在所有组合中AI检测敏感度提升17.14%，精度影响微小。
- 适合需要高灵敏度病理辅助的AI研究与临床应用。
Z-stack扫描是一种新兴的全切片成像技术，可沿玻璃切片的z轴捕捉多个焦平面。相比单层全切片成像，该技术能提供更丰富的深度信息，特别适用于分析微小的组织病理模式。然而其实际临床价值仍存在争议，结果不一。为澄清这一问题，我们研究了Z-stack扫描对脑膜瘤人工智能（AI）有丝分裂检测的影响。使用同一组22张H&E染色脑膜瘤玻片，由三种数字病理扫描仪分别生成单层与多焦层全切片图像（WSIs），测试三种AI流程在两种图像上的表现。结果显示，在所有扫描仪-AI组合中，多焦层WSIs均显著提升AI对有丝分裂的敏感度（+17.14%），而对精确度影响极小。研究提供了量化证据，表明Z-stack扫描是提升AI有丝分裂检测性能的有前景技术，有助于构建更可靠的AI辅助病理工作流，最终改善患者管理。
原文摘要 · Abstract (English)
Z-stack scanning is an emerging whole slide imaging technology that captures multiple focal planes alongside the z-axis of a glass slide. Because z-stacking can offer enhanced depth information compared to the single-layer whole slide imaging, this technology can be particularly useful in analyzing small-scaled histopathological patterns. However, its actual clinical impact remains debated with mixed results. To clarify this, we investigate the effect of z-stack scanning on artificial intelligence (AI) mitosis detection of meningiomas. With the same set of 22 Hematoxylin and Eosin meningioma glass slides scanned by three different digital pathology scanners, we tested the performance of three AI pipelines on both single-layer and z-stacked whole slide images (WSIs). Results showed that in all scanner-AI combinations, z-stacked WSIs significantly increased AI's sensitivity (+17.14%) on the mitosis detection with only a marginal impact on precision. Our findings provide quantitative evidence that highlights z-stack scanning as a promising technique for AI mitosis detection, paving the way for more reliable AI-assisted pathology workflows, which can ultimately benefit patient management.
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