Computational normalization of H&E-stained histological images: Progress, challenges and future potential.
Different types of cancer can be diagnosed with the analysis of histological samples stained with hematoxylin-eosin (H&E). Through this stain, it is possible to identify the architecture of tissue components and analyze cellular morphological aspects that are essential for cancer diagnosis. However,...
| Publicado en: | Artificial Intelligence in Medicine Vol. 95; pp. 118 - 133 |
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| Autores principales: | , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135437564&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135437564 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Apr2019 vid: 95 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135437564 135437564 NLM30420242 135437564 10.1016/j.artmed.2018.10.004 NLM30420242 135437564 ppf: 118 ppct: 15 formats: tig: atl: Computational normalization of H&E-stained histological images: Progress, challenges and future potential. aug: au: Azevedo Tosta, Thaína A. de Faria, Paulo Rogério Neves, Leandro Alves do Nascimento, Marcelo Zanchetta affil: Center of Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil sug: subj: Staining and Labeling Methods Neoplasms Pathology Color Algorithms Artifacts ab: Different types of cancer can be diagnosed with the analysis of histological samples stained with hematoxylin-eosin (H&E). Through this stain, it is possible to identify the architecture of tissue components and analyze cellular morphological aspects that are essential for cancer diagnosis. However, preparation and digitization of histological samples can lead to color variations that influence the performance of segmentation and classification algorithms in histological image analysis systems. Among the determinant factors of these color variations are different staining time, concentration and pH of the solutions, and the use of different digitization systems. This has motivated the development of normalization algorithms of histological images for their color adjustments. These methods are designed to guarantee that biological samples are not altered and artifacts are not introduced in the images, thus compromising the lesions diagnosis. In this context, normalization techniques are proposed to minimize color variations in histological images, and they are topics covered by important studies in the literature. In this proposal, it is presented a detailed study of the state of art of computational normalization of H&E-stained histological images, highlighting the main contributions and limitations of correlated works. Besides, the evaluation of normalization methods published in the literature are depicted and possible directions for new methods are described. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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