Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.
Laboratoire d'Informatique Paris Descartes (LIPADE), Université de Paris, Paris, France.
Br J Pharmacol. 2021 Nov;178(21):4291-4315. doi: 10.1111/bph.15633. Epub 2021 Sep 13.
Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information due to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied to routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumour histopathology. LINKED ARTICLES: This article is part of a themed issue on Molecular imaging - visual themed issue. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.21/issuetoc.
肿瘤诊断依赖于病理学家通过显微镜目镜对组织学载玻片进行的目视检查。数字病理学是通过幻灯片扫描仪对高倍放大的组织学幻灯片进行数字化,由于图像分析,它提供了提取定量信息的机会。在过去十年中,由于人工智能 (AI) 算法的发展,医学图像分析取得了非凡的进展。AI 已成功应用于医学成像领域,最近也应用于数字病理学。在过去的几年中,已经证明了 AI 辅助病理任务的可行性和有用性,我们可以预期这些发展将在未来应用于常规组织病理学。在这篇综述中,我们将描述和说明这项技术,并介绍肿瘤组织病理学领域的最新应用。相关文章:本文是分子影像学主题专刊的一部分 - 视觉主题专刊。要查看本节中的其他文章,请访问 http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.21/issuetoc.