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.
翻译：已经为H&E图像的核分离开发了许多基于深层次学习的方法,这些方法已经接近人类的性能,然而,将这类方法直接应用到另一种图像模式,如Immunohistechicistry(IHC)图像,可能无法取得令人满意的性能。因此,我们开发了一种基于基因的反向网络(GAN)方法,将IHC图像转化为H&E图像,同时保留核心位置和形态学,然后对虚拟H&E图像应用预先训练的核分离模型。 我们证明,拟议方法比几种基线方法效果更好,包括直接应用诸如Cellpose和HVer-Net(Cellpose)等艺术核分离法状态和GeepLIIF(TeepLIF,使用两个公开的IHC图像数据集)。
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