Assessing the degree of liver fibrosis is fundamental for the management of patients with chronic liver disease, in liver transplants procedures, and in general liver disease research. The fibrosis stage is best assessed by histopathologic evaluation, and Masson’s Trichrome stain (MT) is the stain of choice for this task in many laboratories around the world. However, the most used stain in histopathology is Hematoxylin Eosin (HE) which is cheaper, has a faster turn-around time and is the primary stain routinely used for evaluation of liver specimens. In this paper, we propose a novel digital pathology system that accurately detects and quantifies the footprint of fibrous tissue in HE whole slide images (WSI). The proposed system produces virtual MT images from HE using a deep learning model that learns deep texture patterns associated with collagen fibers. The training pipeline is based on conditional generative adversarial networks (cGAN), which can achieve accurate pixel-level transformation. Our comprehensive training pipeline features an automatic WSI registration algorithm, which qualifies the HE/MT training slides for the cGAN model. Using liver specimens collected during liver transplantation procedures, we conducted a range of experiments to evaluate the detected footprint of selected anatomical features. Our evaluation includes both image similarity and semantic segmentation metrics. The proposed system achieved enhanced results in the experiments with significant improvement over the state-of-the-art CycleGAN learning style, and over direct prediction of fibrosis in HE without having the virtual MT step.

评估肝纤维化程度对于慢性肝病患者的管理、肝移植手术和一般肝病研究至关重要。纤维化阶段最好通过组织病理学评估来评估，而 Masson 的三色染色 (MT) 是世界上许多实验室为此任务选择的染色。然而，组织病理学中最常用的染色剂是苏木精伊红 (HE)，它更便宜，周转时间更快，是常规用于评估肝脏标本的主要染色剂。在本文中，我们提出了一种新颖的数字病理学系统，可以准确检测和量化 HE 全幻灯片图像 (WSI) 中纤维组织的足迹。拟议的系统使用深度学习模型从 HE 生成虚拟 MT 图像，该模型学习与胶原纤维相关的深层纹理模式。训练管道基于条件生成对抗网络（cGAN），可以实现精确的像素级转换。我们全面的培训管道具有自动 WSI 注册算法，该算法使 HE/MT 培训幻灯片符合 cGAN 模型的要求。我们使用在肝移植过程中收集的肝脏标本进行了一系列实验，以评估检测到的选定解剖特征的足迹。我们的评估包括图像相似性和语义分割指标。所提出的系统在实验中取得了增强的结果，与最先进的 CycleGAN 学习风格相比有了显着改进，