Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images
Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images
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DOI：
10.1186/s13000-020-01003-0
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发表时间：
2020-07-28
影响因子：
2.6
通讯作者：
Saltz, Joel
中科院分区：
文献类型：
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作者：
Fassler, Danielle J.;Abousamra, Shahira;Saltz, Joel
背景多重免疫组化（mIHC）允许在单个组织切片中标记六种或更多种不同的细胞类型。每种细胞类型的分类需要检测定位于表达感兴趣的生物标志物的细胞的独特有色色原。评估此类载玻片的最全面和可重复的方法是将数字病理学和图像分析管道应用于全载玻片图像（WSIs）。我们的一套深度学习工具定量评估了mIHC WSIs中六种生物标志物的表达。这些方法解决了目前缺乏容易获得的方法来评估超过四种生物标志物，并避免了对专门仪器的需要，以光谱分离不同的颜色。我们的方法的用例应用是一项研究，该研究使用定制的mIHC面板研究胰腺导管腺癌（PDAC）中的肿瘤免疫相互作用。方法用6种不同颜色的色原分别标记福尔马林固定石蜡包埋（FFPE）PDAC组织切片中的T细胞（CD 3、CD 4、CD 8）、B细胞（CD 20）、巨噬细胞（CD 16）和肿瘤细胞（K17）。我们利用病理学家注释来开发互补的基于深度学习的方法：（1）ColorAE是一种基于颜色分割染色对象的深度自动编码器;（2）U-Net是一种卷积神经网络（CNN），经过训练可以基于颜色，纹理和形状分割细胞;以及采用ColorAE和U-Net的集成方法，统称为（3）ColorAE：U-Net。我们使用以下方法评估了我们方法的性能：结构相似性和DICE评分，以评估ColorAE对传统颜色去卷积的分割结果; F1评分，灵敏度，阳性预测值和DICE评分，以评估ColorAE，U-Net和ColorAE的预测：U-Net集成方法对病理学家生成的地面真相。然后，我们使用预测结果进行空间分析（最近邻）。结果我们观察到：（1）对于单染色IHC图像，ColorAE的性能与传统的彩色去卷积相当（注意：传统的颜色去卷积不能用于mIHC）;（2）ColorAE和U-Net是检测6种不同类型细胞的互补方法，具有相当的性能;（3）ColorAE和U-Net组合成集成方法优于单独使用ColorAE和U-Net;（4）ColorAE：U-Net集成方法可用于肿瘤微环境（TME）的详细分析。我们开发了一套可扩展的深度学习方法来分析mIHC WSI中的6个不同标记的细胞群。我们评估了我们的方法，发现它们可靠地检测和分类PDAC肿瘤微环境中的细胞。我们还提出了一个用例，其中我们在3个mIHC WSI中应用ColorAE：U-Net集成方法，并使用预测来量化所有染色的细胞群并进行最近邻空间分析。因此，我们提供了概念证明，这些方法可以用于定量描述肿瘤微环境中免疫细胞的空间分布。这些互补的深度学习方法可随时部署用于临床研究。
Background Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. Methods Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1)ColorAEis a deep autoencoder which segments stained objects based on color; (2)U-Netis a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ bothColorAEandU-Net, collectively referred to as (3)ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor). Results We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.