Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks
Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks
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DOI：
10.1109/tmi.2021.3069874
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发表时间：
2021-08-01
影响因子：
10.6
通讯作者：
He, Yonghong
中科院分区：
文献类型：
--
作者：
Liu, Shuting;Zhang, Baochang;He, Yonghong
病理检查是诊断癌症的金标准。常见的病理学检查包括苏木精-伊红（H&E）染色和免疫组织化学（IHC）。在某些情况下，仅参考H&E染色图像很难准确诊断癌症。而免疫组化检测可以进一步为诊断过程提供足够的证据。因此，从H& E染色图像生成虚拟IHC图像将是当前IHC检查难以访问的问题的很好解决方案，特别是对于一些资源匮乏的地区。然而，现有的方法在微观结构保存和病理学性质的一致性方面存在局限性。此外，像素级配对数据难以获得。在我们的工作中，我们提出了一种新的对抗性学习方法，用于从相应的H& E染色图像生成有效的Ki-67染色图像。该方法充分利用结构相似性约束和跳跃连接来提高结构细节的保留;首次提出病理一致性约束和病理表示网络来强制生成图像和源图像在不同染色域保持相同的病理属性。我们经验证明了我们的方法在两个不同的未配对的组织病理学数据集的有效性。大量的实验表明，我们的方法的上级性能，超过了国家的最先进的方法的显着保证金。此外，我们的方法在非平衡数据集上也取得了稳定和良好的性能，这表明我们的方法具有很强的鲁棒性。我们相信，我们的方法在临床虚拟染色和推进计算机辅助多染色组织学图像分析的进展具有显着的潜力。
Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&E-stained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.