在纤维组织中通过深度神经网络跟踪原蛋白纤维中心线,以变异性自编码器为基础的合成训练数据生成
Hyojoon Park1, Bin Li2, Yuming Liu3
1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA; Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53706, USA.
Medical image analysis
|October 6, 2023
概括
这项研究引入了一种深度学习管道,以量化患病组织中的原纤维组织. 该方法使用合成数据来提高分析原蛋白的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 计算病理学计算病理学
- 医学成像分析 医学成像分析
背景情况:
- 纤维状原组织在癌症和炎症等疾病中至关重要.
- 量化原纤维拓学有助于理解疾病进展.
- 有限的注释数据阻碍了准确的原纤维分析.
研究的目的:
- 开发一个深度学习管道来量化原蛋白纤维的拓性质.
- 使用生成模型解决注释数据的短缺问题.
- 为了能够对原纤维的方向,对齐,密度和长度进行定量测量.
主要方法:
- 一个深度学习管道,结合了变量自动编码器和条件生成对抗网络.
- 生成合成原纤维中心线和相应的图像用于训练数据.
- 使用真实和合成数据训练一个原纤维中线提取网络.
主要成果:
- 该管道在病理组织图像中准确量化原蛋白纤维的拓性质.
- 该方法的性能优于现有的纤维中心线提取工具.
- 整合合成数据显著提高了网络的通用性.
结论:
- 开发的深度学习管道为分析纤维状原蛋白组织提供了强大的方法.
- 合成数据的使用可以提高模型的性能和在疾病研究中的应用性.
- 这种方法促进了对原纤维的定量分析,以改善疾病的理解.
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