Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas

TL;DR

Z-stack scanning enhances AI's ability to detect mitoses in meningiomas by providing additional depth information, significantly increasing sensitivity with minimal impact on precision, thus improving pathology workflows.

Contribution

This study provides the first quantitative evidence that z-stack scanning improves AI mitosis detection accuracy in histopathology.

Findings

Z-stack scanning significantly increases AI sensitivity (+17.14%)

Minimal impact on AI precision with z-stacking

Consistent improvement across different scanners and AI pipelines

Abstract

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.