结构上受约束和病理意识的卷积变压器生成对抗网络用于虚拟组织学染色人体冠状动脉光学连贯断层扫描图像的虚拟组织学染色
Xueshen Li1,2, Hongshan Liu1,2, Xiaoyu Song3
1Stevens Institute of Technology, Department of Biomedical Engineering, Hoboken, New Jersey, United States.
Journal of biomedical optics
|March 27, 2024
概括
这项研究介绍了SCPAT-GAN,这是一种新的方法,可以从无配对数据的冠状动脉OCT图像中生成虚拟组织学. 这一进步有助于通过可视化病理模式来诊断冠状动脉疾病 (CAD).
科学领域:
- 医疗成像医学成像
- 计算病理学计算病理学
- 人工智能的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 诊断需要详细的组织学信息,通常通过侵入性方法获得.
- 目前从光学连贯断层扫描 (OCT) 图像中生成虚拟组织学,但面临着局限性,包括需要大型像素智能配对数据集和受限制的病理区域映射.
研究的目的:
- 开发一种方法,可以从冠状动脉OCT数据中生成虚拟组织学图像,而不需要像素智能配对训练数据集.
- 从OCT图像中可视化冠状动脉中的病理模式,以改善CAD评估.
主要方法:
- 一个结构约束,病理意识的变压器生成对抗网络 (SCPAT-GAN) 旨在从OCT图像中生成虚拟的H&E组织学.
- 定量评估涉及Fréchet初始距离 (FID) 和感知哈希值 (PHV) 的指标.
- 定性评估包括专家病理学家的审查和对生成图像的视觉检查,以及一个废除研究.
主要成果:
- 与现有方法相比,SCPAT-GAN取得了更高的FID和PHV得分.
- 生成的虚拟组织学图像准确地描绘了正常和病理特征,没有文物,视觉检查和专家病理学家证实了这一点.
- 废除研究验证了综合病理意识和结构约束模块的有效性.
结论:
- SCPAT-GAN成功地展示了从OCT图像中生成具有正常和病态模式的虚拟组织学,从而消除了对像素智能监督训练的需求.
- 预计这项技术将通过提供先进的二维和三维组织病理可视化来增强CAD治疗中的临床决策.
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