使用虚拟染色转换组织病理学的新进展。

IF 5 Q1 ENGINEERING, BIOMEDICAL
BME frontiers Pub Date : 2020-08-25 eCollection Date: 2020-01-01 DOI:10.34133/2020/9647163
Yair Rivenson, Kevin de Haan, W Dean Wallace, Aydogan Ozcan
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引用次数: 51

摘要

在数字化在临床和临床前工作流程中广泛存在的时代,病理学仍然主要通过对粘贴在载玻片上的染色组织样本进行显微镜评估来实践。在过去的十年里,新型高通量数字扫描显微镜开创了数字病理学时代,随着机器视觉的最新进展,为计算机辅助诊断开辟了新的可能性。尽管取得了这些进展,但与数字病理学相关的高昂基础设施成本,以及人们认为数字化过程是一个额外的、不可直接补偿的步骤,对其广泛采用提出了挑战。在这里,我们讨论了新兴的虚拟染色技术和机器学习如何有助于打破标准的组织病理学工作流程,并为诊断范式创造新的途径,通过数字病理学使患者和医疗系统都受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Emerging Advances to Transform Histopathology Using Virtual Staining.

Emerging Advances to Transform Histopathology Using Virtual Staining.

Emerging Advances to Transform Histopathology Using Virtual Staining.

Emerging Advances to Transform Histopathology Using Virtual Staining.

In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.

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来源期刊
CiteScore
7.10
自引率
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审稿时长
16 weeks
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