DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides.

Parmida Ghahremani, Joseph Marino, Ricardo Dodds, Saad Nadeem
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引用次数: 0

Abstract

In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.

Abstract Image

Abstract Image

DeepLIIF:临床病理切片定量的在线平台。
在临床上,切除的组织样本用苏木精和伊红(H&E)和/或免疫组织化学(IHC)染色,并在玻片上或作为数字扫描呈现给病理学家,以诊断和评估疾病进展。细胞水平的定量,例如免疫结构蛋白表达评分,可能非常低效和主观。我们提出DeepLIIF (https://deepliif.org),这是第一个用于高效和可重复IHC评分的免费在线平台。DeepLIIF优于目前最先进的方法(依赖于人工容易出错的注释),通过更多信息的多重免疫荧光染色几乎保留临床IHC玻片。我们的DeepLIIF云原生平台支持(1)通过Bio-Formats标准支持150多种专有/非专有输入格式,(2)交互式调整、可视化和下载IHC量化结果和随附的保留图像,(3)以编程方式或通过开源整张幻灯片图像查看器(如QuPath/ImageJ)的交互式插件使用暴露的工作流API,以及(4)自动缩放以根据用户需求有效缩放GPU资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
43.50
自引率
0.00%
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