Z-Stack 扫描可以改进 AI 对有丝分裂的检测：脑膜瘤案例研究
Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas
AI总结：
本研究针对脑膜瘤，探讨了 Z-Stack 扫描技术对 AI 有丝分裂检测的影响，发现其能显著提升检测灵敏度且对精确度影响极小，为更可靠的 AI 辅助病理工作流程提供了定量证据。
摘要：
Z-stack 扫描是一项新兴的全切片成像技术，能够沿着玻璃切片的 z 轴捕获多个焦平面。由于与单层全切片成像相比，z-stack 能够提供增强的深度信息，这项技术在分析小尺度的组织病理学模式时尤为有用。然而，其实际的临床影响仍存在争议，研究结果褒贬不一。为了澄清这一点，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。使用由三台不同数字病理扫描仪扫描的同一组 22 张苏木精和伊红脑膜瘤玻璃切片，我们在单层和 z-stack 全切片图像（WSIs）上测试了三种 AI 流水线的性能。结果显示，在所有扫描仪与 AI 的组合中，z-stack WSIs 显著提高了 AI 对有丝分裂检测的灵敏度（+17.14%），且对精确度的影响微乎其微。我们的发现提供了定量证据，表明 z-stack 扫描是一项极具前景的 AI 有丝分裂检测技术，为更可靠的 AI 辅助病理学工作流程铺平了道路，最终将使患者管理受益。
英文摘要：
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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