通过染色解卷积和复染 HE-HES 实现 HE 染色切片上的胶原蛋白定量
Enabling Collagen Quantification on HE-stained Slides Through Stain Deconvolution and Restained HE-HES
AI总结：
该研究利用配准复染 HE-HES 数据和染色解卷积构建真值，训练 UNet 从常规 HE 图像预测藏红密度，从而实现胶原蛋白定量与数字 HES 生成。
摘要：
在组织学中，细胞外基质中胶原蛋白的存在对癌症恶性程度具有诊断和预后价值，并且可以通过在常规苏木精-伊红（HE）染色中加入藏红（Saffron，S）来突显。然而，由于额外成本以及病理学家习惯使用 HE，除法国的实验室外，通常不会添加藏红。本文表明，仅从 HE 图像中定量胶原蛋白含量并以数字方式生成 HES 图像是可能的。为此，我们训练了一个 UNet，以根据 HE 图像预测藏红密度。我们创建了一个配准的、复染 HE-HES 切片数据集，并通过对 HES 图像进行染色解卷积来提取藏红浓度作为真值。我们的模型在 3 折测试集上达到了 0.0668 ± 0.0002 的平均绝对误差（藏红值介于 0 和 1 之间）。我们希望该方法有助于改善临床工作流程，同时降低实验室的试剂成本。
英文摘要：
In histology, the presence of collagen in the extra-cellular matrix has both diagnostic and prognostic value for cancer malignancy, and can be highlighted by adding Saffron (S) to a routine Hematoxylin and Eosin (HE) staining. However, Saffron is not usually added because of the additional cost and because pathologists are accustomed to HE, with the exception of France-based laboratories. In this paper, we show that it is possible to quantify the collagen content from the HE image alone and to digitally create an HES image. To do so, we trained a UNet to predict the Saffron densities from HE images. We created a dataset of registered, restained HE-HES slides and we extracted the Saffron concentrations as ground truth using stain deconvolution on the HES images. Our model reached a Mean Absolute Error of 0.0668 $\pm$ 0.0002 (Saffron values between 0 and 1) on a 3-fold testing set. We hope our approach can aid in improving the clinical workflow while reducing reagent costs for laboratories.
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