基于双重对比学习的图像对图像翻译,将未染色的皮肤组织转化为几乎染色的H&E图像
Muhammad Zeeshan Asaf1, Babar Rao2,3, Muhammad Usman Akram4
1Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Scientific reports
|January 28, 2024
概括
这项研究引入了一种新的AI模型,用于虚拟血素和素 (H&E) 染色组织图像. 生成模型准确地模拟了H&E污点,为传统的组织病理学方法提供了更快,更安全,更可持续的替代方案.
科学领域:
- 组织病理学 组织病理学
- 数字病理学数字病理学
- 人工智能在医学中的应用
背景情况:
- 血素和欧 (H&E) 染色是组织病理学的基石,对显微镜组织检查和诊断至关重要.
- 传统的H&E染色是耗时的,使用危险化学品,并且可以具有可变的准确性.
- 生成型人工智能 (AI) 和深度学习为增强基因病理学工作流提供了潜在的解决方案.
研究的目的:
- 开发和评估一种新的生成AI模型,用于虚拟H&E染色未染色的皮肤组织图像.
- 评估模型能够准确地复制传统H&E染料的视觉特征的能力.
- 探索虚拟染色的潜力,作为传统方法的替代方案,旨在提高效率和减少对环境的影响.
主要方法:
- 引入一个三阶段的,基于双对比的学习,图像对图像生成 (DCLGAN) 模型.
- 使用一个独特的学习环境,具有两对生成器和区分器.
- 采用对比学习来最大限度地提高传统的H&E染色和虚拟染色图像补丁之间的相互信息.
- 数据集包括在20倍放大下获得的配对未染色和H&E染色的皮肤组织图像.
- 使用Fréchet Inception Distance (FID) 和Kernel Inception Distance (KID) 的指标进行定量评估.
- 由经验丰富的皮肤病理学家进行定性评估,评估图像的准确性和一致性.
主要成果:
- DCLGAN模型在虚拟H&E染色中表现出高保真度,与未染色图像相比,FID和KID得分明显较低.
- 虚拟染色和H&E染色图像之间的平均FID为80.47,相比之下342.01 (不染色与虚拟) 和320.4 (不染色与H&E).
- H&E染色和虚拟染色图像之间的平均KID为0.022,相比之下是0.28 (无染色与H&E) 和0.31 (无染色与虚拟).
- 皮肤病理学家的同意率很高:对联虚拟图像评估的同意率为78.8%,对单个虚拟图像评估的同意率为90.2%.
- 该模型有效地生成了与传统染色相比的虚拟H&E染色图像.
结论:
- 拟议的DCLGAN模型对于生成虚拟的H&E染色基因病理图像是有效的.
- 虚拟染色显示显著的希望作为一个可行的和潜在的优越替代传统的H&E染色.
- 人工智能驱动的虚拟染色可以缩短处理时间,最大限度地减少危险化学品的使用,并减轻基因病理学对环境的影响.
- 这项技术有可能通过提供更有效,更安全和更可持续的诊断方法来彻底改变组织病理学.
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