Virtual staining for histology by deep learning
Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Trends in biotechnology - (2024) vom: 13. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Latonen, Leena [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence (AI) |
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| Anmerkungen: |
Date Revised 13.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.tibtech.2024.02.009 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM369697340 |
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| 520 | |a 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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