Leena Latonen, Sonja Koivukoski, Umair Khan, Pekka Ruusuvuori
{"title":"Virtual staining for histology by deep learning.","authors":"Leena Latonen, Sonja Koivukoski, Umair Khan, Pekka Ruusuvuori","doi":"10.1016/j.tibtech.2024.02.009","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23324,"journal":{"name":"Trends in biotechnology","volume":null,"pages":null},"PeriodicalIF":14.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.tibtech.2024.02.009","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/13 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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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