[论文解读] Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue

Yair Rivenson, Hongda Wang+4 more|arXiv (Cornell University)|Mar 30, 2018

Cell Image Analysis Techniques参考文献 36被引用 162

一句话总结

本论文展示了一种深度学习方法，从未标记的组织自发荧光生成几乎被染色的组织学图像，绕过传统染色。

ABSTRACT

Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious preparation, which stains the tissue to visualize different histological features under a microscope. Here, we demonstrate a label-free approach to create a virtually-stained microscopic image using a single wide-field auto-fluorescence image of an unlabeled tissue sample, bypassing the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neural network trained using a generative adversarial network model to transform an auto-fluorescence image of an unlabeled tissue section into an image that is equivalent to the bright-field image of the stained-version of the same sample. We validated this method by successfully creating virtually-stained microscopic images of human tissue samples, including sections of salivary gland, thyroid, kidney, liver and lung tissue, also covering three different stains. This label-free virtual-staining method eliminates cumbersome and costly histochemical staining procedures, and would significantly simplify tissue preparation in pathology and histology fields.

研究动机与目标

• 通过去除传统染色步骤实现更快、更具成本效益的组织学。
• 开发一个无标记成像流程，产生与染色等效的图像。
• 利用深度学习将自发荧光映射到明场组织学表示。

提出的方法

• 以未标记组织的宽场自发荧光成像作为输入。
• 在生成对抗网络框架内训练卷积神经网络。
• 将自发荧光图像转换为在视觉上类似于常规组织学的虚拟染色明场输出。
• 通过配对自发荧光与相应的染色参考进行有监督学习。
• 在跨多种器官和染色的人体组织样本上验证该方法。

实验结果

研究问题

• RQ1 未标记组织的自发荧光图像是否可以转换为在视觉上准确的虚拟染色组织学图像？
• RQ2 CNN-GAN 模型是否能够在不同组织类型和组织化学染色之间实现泛化？
• RQ3 与实际染色图像相比，虚拟染色图像在保留诊断相关特征方面的表现如何？

主要发现

• 成功从未标记组织的自发荧光中创建了虚拟染色的显微图像。
• 在人类组织样本上进行验证，包括唾液腺、甲状腺、肾、肝和肺。
• 证明可适用于三种不同的组织化学染色。
• 在病理流程中消除了繁琐的组织化学染色程序的需要。