Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.
Institute of Translational and Clinical Medicine, Newcastle University Medical School, Newcastle upon Tyne, UK.
J Eur Acad Dermatol Venereol. 2023 Mar;37(3):605-614. doi: 10.1111/jdv.18726. Epub 2022 Dec 3.
The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work-streams required for tissue-specific (skin) and immunostain-specific annotation has not been extensively studied compared with the development of AI algorithms.
The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI-assisted analysis pipeline.
A total of 45 slides containing 3-5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells.
We validated two DL models using 10-fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells.
The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.
人工智能（AI）在全切片图像中的应用有可能提高研究的可靠性，并最终提高诊断效率和服务能力。图像标注在 AI 和数字病理学中起着关键作用。然而，与 AI 算法的开发相比，针对组织特异性（皮肤）和免疫染色特异性的标注所需的工作流程尚未得到广泛研究。
本研究的目的是开发一种通用工作流程，用于在开发 AI 辅助分析管道之前，对炎症性皮肤病活检的全切片图像进行各种表皮和真皮标记的免疫染色进行标注。
共扫描了 45 张载玻片，每张载玻片包含 3-5 个切片，使用 Aperio AT2 载玻片扫描仪（Leica Biosystems）进行扫描。这些切片使用常用的图像分析工具进行手动标注，共生成了超过 4000 个图像块。我们使用深度学习（DL）方法首先顺序分割（表皮和上真皮），排除常见的伪影，然后对皮肤活检的这两个部位的免疫染色信号和阳性细胞的比例进行定量。
我们使用 10 倍验证运行和与手动标注数据的比较，验证了两个 DL 模型。这些模型在表皮和真皮的分割方面的平均（全局）准确率为 95.0%，在阳性/阴性细胞的分割方面的准确率为 86.1%。
两个 DL 模型的顺序应用有助于准确分割表皮和真皮结构，排除常见伪影，并实现免疫染色信号的定量分析。然而，用于训练 DL 模型的幻灯片标注不准确会降低输出的准确性。我们的开源代码将促进在不同的免疫染色平台和载玻片扫描仪上进行进一步的外部验证。
Zotero 插件
