论文标题
基于图像翻译的核分割用于免疫组织化学图像
Image Translation Based Nuclei Segmentation for Immunohistochemistry Images
论文作者
论文摘要
已经开发了许多基于深度学习的方法，用于用于H＆E图像的核分割，并已接近人类的表现。但是，将这种方法直接应用于另一种图像方式，例如免疫组织化学（IHC）图像，可能无法实现令人满意的性能。因此，我们开发了一种基于生成的对抗网络（GAN）方法，将IHC图像转换为H＆E图像，同时保留核位置和形态，然后将预训练的核分割模型应用于虚拟H＆E图像。我们证明了所提出的方法比几种基线方法更好地工作，包括直接应用对H＆E进行培训的TART核分割方法，例如Cellpose和Hover-NET，并使用两个公共IHC图像数据集进行了培训。
Numerous deep learning based methods have been developed for nuclei segmentation for H&E images and have achieved close to human performance. However, direct application of such methods to another modality of images, such as Immunohistochemistry (IHC) images, may not achieve satisfactory performance. Thus, we developed a Generative Adversarial Network (GAN) based approach to translate an IHC image to an H&E image while preserving nuclei location and morphology and then apply pre-trained nuclei segmentation models to the virtual H&E image. We demonstrated that the proposed methods work better than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H&E and a generative method, DeepLIIF, using two public IHC image datasets.
