论文标题
通过未配对的图像到图像翻译gan模型转换为H＆E组织学模态转换为H＆E组织学模态的显微镜
Slide-free MUSE Microscopy to H&E Histology Modality Conversion via Unpaired Image-to-Image Translation GAN Models
论文作者
论文摘要
缪斯（Muse）是一种新型的无幻觉成像技术，用于对组织的组织学检查，可以作为传统组织学的替代方法。为了弥合缪斯和传统组织学之间的鸿沟，我们旨在将缪斯图像转换为类似于正宗的苏木精和曙红染色（H＆E）图像。我们评估了四个模型：一种基于非细节的基于颜色的颜色映射的基于Unmixing的工具，即Cyclegan，Dualgan和Ganilla。 Cyclegan和Ganilla提供了令人信服的结果，可适当地转移H＆E风格和保存的Muse内容。根据培训对实际和生成的H＆E图像的自动评论家，我们确定Cyclegan表现出了最佳性能。我们还发现，缪斯颜色反演可能是准确转换为H＆E的必要步骤。我们认为，我们的博物馆对H＆E模型可以通过弥合Muse Imaging和传统组织学之间的感知差距来帮助改善新颖的无幻灯片方法的采用。
MUSE is a novel slide-free imaging technique for histological examination of tissues that can serve as an alternative to traditional histology. In order to bridge the gap between MUSE and traditional histology, we aim to convert MUSE images to resemble authentic hematoxylin- and eosin-stained (H&E) images. We evaluated four models: a non-machine-learning-based color-mapping unmixing-based tool, CycleGAN, DualGAN, and GANILLA. CycleGAN and GANILLA provided visually compelling results that appropriately transferred H&E style and preserved MUSE content. Based on training an automated critic on real and generated H&E images, we determined that CycleGAN demonstrated the best performance. We have also found that MUSE color inversion may be a necessary step for accurate modality conversion to H&E. We believe that our MUSE-to-H&E model can help improve adoption of novel slide-free methods by bridging a perceptual gap between MUSE imaging and traditional histology.
