Histological slides contain numerous artifacts that can significantly deteriorate the performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and multi-class artifact segmentation. GrandQC allows for high-precision tissue segmentation (Dice score 0.957) and segmentation of tissue without artifacts (Dice score 0.919–0.938 dependent on magnification). Slides from 19 international pathology departments digitized with the most common scanning systems and from The Cancer Genome Atlas dataset were used to establish a QC benchmark, analyzing inter-institutional, intra-institutional, temporal, and inter-scanner slide quality variations. GrandQC improves the performance of downstream image analysis algorithms. We open-source the GrandQC tool, our large manually annotated test dataset, and all QC masks for the entire TCGA cohort to address the problem of QC in digital/computational pathology. GrandQC can be used as a tool to monitor sample preparation and scanning quality in pathology departments and help to track and eliminate major artifact sources.

组织学载玻片包含许多伪影，这些伪影会显著降低图像分析算法的性能。在这里，我们开发了用于组织和多类伪影分割的 GrandQC 工具。GrandQC 允许高精度组织分割（Dice 分数 0.957）和无伪影的组织分割（Dice 分数 0.919–0.938，取决于放大倍率）。使用最常见的扫描系统对来自 19 个国际病理学部门的载玻片和癌症基因组图谱数据集的载玻片进行数字化处理，以建立 QC 基准，分析机构间、机构内、时间和扫描仪间载玻片质量的变化。GrandQC 提高了下游图像分析算法的性能。我们开源了 GrandQC 工具、我们的大型手动注释测试数据集以及整个 TCGA 队列的所有 QC 掩码，以解决数字/计算病理学中的 QC 问题。GrandQC 可用作监测病理科样本制备和扫描质量的工具，并帮助跟踪和消除主要伪影来源。