Shenzhen International Graduate School, Tsinghua University, Xili University City, Shenzhen, 518055, Guangdong, China.
The Third People's Hospital of Shenzhen, Buji Buran Road 29, Shenzhen, 518112, Guangdong, China.
Comput Med Imaging Graph. 2023 Apr;105:102185. doi: 10.1016/j.compmedimag.2023.102185. Epub 2023 Jan 23.
Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring. However, the expensive costs of time, economy, and patient specimens as well as the non-uniform preparation and staining process make the conversion of existing H&E staining into virtual Masson trichrome staining a solution for fibrosis evaluation. Existing translation approaches fail to extract fiber features accurately enough, and the decoder of staining is unable to converge due to the inconsistent color of physical staining. In this work, we propose a prior-guided generative adversarial network, based on unpaired data for effective Masson trichrome stained image generation from the corresponding H&E stained image. Conducted on a small training set, our method takes full advantage of prior knowledge to set up better constraints on both the encoder and the decoder. Experiments indicate the superior performance of our method that surpasses the previous approaches. For various liver diseases, our results demonstrate a high correlation between the staging of real and virtual stains (ρ=0.82; 95% CI: 0.73-0.89). In addition, our finetuning strategy is able to standardize the staining color and release the memory and computational burden, which can be employed in clinical assessment.
纤维化是慢性肝病发展过程中的一个必然阶段，对刻画慢性肝病的进展程度具有不可替代的作用。组织病理学诊断是解释纤维化参数的金标准。常规的苏木精-伊红（H&E）染色只能反映组织的大体结构和肝细胞的分布，而 Masson 三色染色可以突出特定类型的胶原纤维结构，从而为纤维化评分提供必要的结构信息。然而，时间、经济和患者标本的昂贵成本，以及不统一的准备和染色过程，使得将现有的 H&E 染色转化为虚拟的 Masson 三色染色成为纤维化评估的一种解决方案。现有的转换方法无法准确地提取纤维特征，而且由于物理染色的颜色不一致，染色解码器无法收敛。在这项工作中，我们提出了一种基于无配对数据的先验引导生成对抗网络，用于从相应的 H&E 染色图像生成有效的 Masson 三色染色图像。在一个小的训练集上进行的实验表明，我们的方法充分利用了先验知识，对编码器和解码器都设置了更好的约束。实验表明，我们的方法具有优越的性能，超过了以前的方法。对于各种肝病，我们的结果表明真实和虚拟染色之间的分期具有高度相关性（ρ=0.82；95%置信区间：0.73-0.89）。此外，我们的微调策略能够标准化染色颜色，释放记忆和计算负担，可用于临床评估。
