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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency
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Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency

机译：无缝虚拟整体幻灯片图像合成和验证使用感知嵌入一致性

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摘要

Stain virtualization is an application with growing interest in digital pathology allowing simulation of stained tissue images thus saving lab and tissue resources. Thanks to the success of Generative Adversarial Networks (GANs) and the progress of unsupervised learning, unsupervised style transfer GANs have been successfully used to generate realistic, clinically meaningful and interpretable images. The large size of high resolution Whole Slide Images (WSIs) presents an additional computational challenge. This makes tilewise processing necessary during training and inference of deep learning networks. Instance normalization has a substantial positive effect in style transfer GAN applications but with tilewise inference, it has the tendency to cause a tiling artifact in reconstructed WSIs. In this paper we propose a novel perceptual embedding consistency (PEC) loss forcing the network to learn color, contrast and brightness invariant features in the latent space and hence substantially reducing the aforementioned tiling artifact. Our approach results in more seamless reconstruction of the virtual WSIs. We validate our method quantitatively by comparing the virtually generated images to their corresponding consecutive real stained images. We compare our results to state-of-the-art unsupervised style transfer methods and to the measures obtained from consecutive real stained tissue slide images. We demonstrate our hypothesis about effect of the PEC loss by comparing model robustness to color, contrast and brightness perturbations and visualizing bottleneck embeddings. We validate the robustness of the bottleneck feature maps by measuring their sensitivity to the different perturbations and using them in a tumor segmentation task. Additionally, we propose a preliminary validation of the virtual staining application by comparing interpretation of 2 pathologists on real and virtual tiles and inter-pathologist agreement.
机译：染色虚拟化是具有越来越多的数字病理兴趣的应用，允许模拟染色组织图像，从而节省了实验室和组织资源。由于生成的对策网络（GANS）的成功以及无监督学习的进展，无监督的风格转移GAN已经成功地用于在现实，临床上有意义和可解释的图像中获得。大尺寸的高分辨率整个幻灯片图像（WSIS）呈现了额外的计算挑战。这使得在深度学习网络的培训和推理期间使瓷砖加工。实例归一化具有在STYLE TRANSIFE GAN应用中具有实质性的积极效果，但是通过曲略化推断，它具有在重建的WSI中导致平铺伪影的趋势。在本文中，我们提出了一种新颖的感知嵌入一致性（PEC）损失强迫网络学习潜在空间中的颜色，对比度和亮度不变特征，因此基本上减少了上述瓷器伪像。我们的方法会导致虚拟WSIS更无缝的重建。通过将几乎生成的图像与其相应的连续实际染色图像进行比较，我们通过将虚拟生成的图像进行比较来定量地验证我们的方法。我们将结果与最先进的无人监督的风格转移方法进行比较，以及从连续真实染色的组织滑动图像获得的措施。我们通过将模型鲁棒性与颜色，对比度和亮度扰动和可视化瓶颈嵌入的模型稳健性进行了展示了我们对PEC损失影响的假设。我们通过测量对不同扰动的敏感性并在肿瘤分割任务中使用它们来验证瓶颈的鲁棒性。此外，我们通过比较2个病理学家对实际和虚拟瓷砖和病理学家间协议的解释提出了虚拟染色应用的初步验证。

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