Pantanowitz Liron, Sharma Ashish, Carter Alexis B, Kurc Tahsin, Sussman Alan, Saltz Joel
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Department of Biomedical Informatics, Emory University, GA, USA.
J Pathol Inform. 2018 Nov 21;9:40. doi: 10.4103/jpi.jpi_69_18. eCollection 2018.
Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.
自全切片成像(WSI)扫描仪商业化推出以来,已过去近20年。在此期间,各种能够将整个玻璃切片数字化的WSI设备的出现改变了病理学领域。计算技术和存储方面的同步进步使得大规模WSI数据集能够得到快速处理。本文概述了与WSI相关的过去和现在的重要工作。介绍了虚拟显微镜如何从地球科学应用中可视化和管理卫星数据的需求演变而来。文章还讨论了从Bacus设计的第一台WSI扫描仪到食品药品监督管理局批准首个用于外科病理学初步诊断的数字病理系统等重要里程碑。随着病理实验室致力于全面数字化,将WSI纳入企业级供应商中立存档(VNA)的需求应运而生。本文回顾了可用的不同类型的VNA,以及如何最好地实施它们,以及病理学如何从参与这项工作中受益。强调了提取像素级、对象级和语义级特征的传统图像算法与深度学习方法之间的差异。还讨论了计算病理学中大规模数据管理、分析和可视化的需求。