Virtual tissue staining in pathology using machine learning
Virtual tissue staining in pathology using machine learning
复制标题
使用机器学习进行病理学虚拟组织染色
DOI:
10.1080/14737159.2022.2153040
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发表时间:
2022
影响因子:
5.1
通讯作者:
Ozcan, Aydogan
中科院分区:
文献类型:
--
作者:
Pillar, Nir;Ozcan, Aydogan
Pathology is a medical discipline dealing with diagnosing and studying diseases. Through recognizing structural histological alterations, pathologists acquire valuable information on the effect of these changes on cellular and tissue function. Pathologist evaluation is performed by examination of histologically stained tissue mounted on a glass slide through an optical microscope, or, in recent years, of a digitized version of the histological image (ie digital whole slide imaging, WSI). The long-established pathology workflow consists of a series of processes to prepare stained tissue samples, which involve fixation, processing, embedding, sectioning and staining [1]. Staining is used to highlight important features of the tissue, as well as to enhance tissue contrast. This is in general a time-consuming, laborious process that needs to be performed in a designated lab infrastructure by trained technicians due to the toxicity of most chemical staining reagents. The semi-automated or manual staining processes and the utilization of different chemical reagents lead to high technical variability in sample preparation, which sometimes causes diagnostic challenges. Furthermore, the staining process distorts the tissue and prohibits additional staining of any specific section and further molecular analysis on the same section. This is highly important in small tissue biopsies of diagnostically challenging cases, where multiple stains are often needed, followed by ancillary tests (eg DNA/RNA sequencing) that may be required to reach a diagnosis. If all the tissue biopsy material is used for staining, such molecular analysis cannot be performed.
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DOI:
10.5539/gjhs.v8n3p72
发表时间:
2015-06-25
期刊:
Global journal of health science
影响因子:
--
作者:
Alturkistani HA;Tashkandi FM;Mohammedsaleh ZM
通讯作者:
Mohammedsaleh ZM
影响因子:
--
作者:
Bai B;Wang H;Li Y;de Haan K;Colonnese F;Wan Y;Zuo J;Doan NB;Zhang X;Zhang Y;Li J;Yang X;Dong W;Darrow MA;Kamangar E;Lee HS;Rivenson Y;Ozcan A
通讯作者:
Ozcan A
DOI:
10.1038/s41377-021-00674-8
发表时间:
2021-11-18
期刊:
Light, science & applications
影响因子:
--
作者:
Li J;Garfinkel J;Zhang X;Wu D;Zhang Y;de Haan K;Wang H;Liu T;Bai B;Rivenson Y;Rubinstein G;Scumpia PO;Ozcan A
通讯作者:
Ozcan A
影响因子:
19.4
作者:
Zhang, Yijie;de Haan, Kevin;Ozcan, Aydogan
通讯作者:
Ozcan, Aydogan
影响因子:
--
作者:
Rivenson Y;de Haan K;Wallace WD;Ozcan A
通讯作者:
Ozcan A