Virtual stain transfer in histology via cascaded deep neural networks
Virtual stain transfer in histology via cascaded deep neural networks
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DOI：
10.1021/acsphotonics.2c00932
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Xilin Yang;Bijie Bai;Yijie Zhang;Yuzhu Li;K. Haan;Tairan Liu;Aydogan Ozcan
中科院分区：
文献类型：
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作者：
Xilin Yang;Bijie Bai;Yijie Zhang;Yuzhu Li;K. Haan;Tairan Liu;Aydogan Ozcan
病理学诊断依赖于组织学染色的薄组织标本的目视检查，其中应用不同类型的染色剂以形成对比并突出显示各种所需的组织学特征。然而，破坏性组织化学染色程序通常是不可逆的，使得很难在同一组织切片上获得多个染色。在这里，我们通过级联深度神经网络（C-DNN）展示了一个虚拟染色转移框架，将苏木精和伊红（H&E）染色的组织图像数字化转换为其他类型的组织学染色。与仅将一种染色类型作为输入以数字输出另一种染色类型图像的单个神经网络结构不同，C-DNN首先使用虚拟染色将自体荧光显微镜图像转换为H&E，然后以级联方式执行从H&E到另一种染色域的染色转移。训练阶段的这种级联结构允许模型直接利用H&E和目标特殊染色的组织化学染色图像数据。这一优势消除了配对数据采集的挑战，并提高了从H&E到另一种染色剂的虚拟染色剂转移的图像质量和颜色准确性。我们使用肾穿刺活检组织切片验证了这种C-DNN方法的上级性能，并成功地将H& E染色的组织图像转换为虚拟PAS（过碘酸-希夫）染色。该方法使用现有的组织化学染色的载玻片提供特殊染色剂的高质量虚拟图像，并通过执行高度精确的染色剂到染色剂转换在数字病理学中创造新的机会。
Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner. This cascaded structure in the training phase allows the model to directly exploit histochemically stained image data on both H&E and the target special stain of interest. This advantage alleviates the challenge of paired data acquisition and improves the image quality and color accuracy of the virtual stain transfer from H&E to another stain. We validated the superior performance of this C-DNN approach using kidney needle core biopsy tissue sections and successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain. This method provides high-quality virtual images of special stains using existing, histochemically stained slides and creates new opportunities in digital pathology by performing highly accurate stain-to-stain transformations.