通过深度学习实现组织学虚拟染色。

IF 14.3 1区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Trends in biotechnology Pub Date : 2024-09-01 Epub Date: 2024-03-13 DOI:10.1016/j.tibtech.2024.02.009
Leena Latonen, Sonja Koivukoski, Umair Khan, Pekka Ruusuvuori
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引用次数: 0

摘要

在病理学和生物医学研究中,组织学是组织分析的基础方法。目前,组织学工作流程在染色过程中需要消耗大量的化学品、水和时间。现在,深度学习能够以数字方式替代部分组织学染色程序。在虚拟染色中,通过训练神经网络从未染色的组织图像中生成染色图像,或将一种染色信息转移到另一种染色信息,从而创建组织学染色。这些技术创新为传统的组织学流程提供了更可持续、更快速、更具成本效益的替代方案,但它们的发展还处于早期阶段,需要经过严格的验证。在这篇综述中,我们将介绍组织学虚拟染色的基本概念,并就人工智能(AI)支持的虚拟组织学的利用提供未来见解。
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Virtual staining for histology by deep learning.

In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.

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来源期刊
Trends in biotechnology
Trends in biotechnology 工程技术-生物工程与应用微生物
CiteScore
28.60
自引率
1.20%
发文量
198
审稿时长
1 months
期刊介绍: Trends in Biotechnology publishes reviews and perspectives on the applied biological sciences, focusing on useful science applied to, derived from, or inspired by living systems. The major themes that TIBTECH is interested in include: Bioprocessing (biochemical engineering, applied enzymology, industrial biotechnology, biofuels, metabolic engineering) Omics (genome editing, single-cell technologies, bioinformatics, synthetic biology) Materials and devices (bionanotechnology, biomaterials, diagnostics/imaging/detection, soft robotics, biosensors/bioelectronics) Therapeutics (biofabrication, stem cells, tissue engineering and regenerative medicine, antibodies and other protein drugs, drug delivery) Agroenvironment (environmental engineering, bioremediation, genetically modified crops, sustainable development).
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