Title: Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings Show Chinese title

Title: 通过生成对抗网络在小样本情况下实现显微镜和磁共振成像之间的风格迁移

Abstract: Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the human corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network's ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes. Show Chinese abstract

Abstract: 基于相同组织样本的磁共振成像(MRI)和显微成像的跨模态增强具有前景，因为它可以在没有基础侵入性活检程序的情况下允许组织病理学分析。 在这里，我们测试了一种方法，该方法使用条件生成对抗网络(cGAN)架构从人类胼胝体的MRI扫描中生成显微组织学图像。 据我们所知，这是首次将脑部MRI转换为同一样本的组织学体积表示的多模态翻译。 该技术通过训练成对的图像翻译模型来评估，这些模型从MRI扫描和显微镜图像中获取图像集。 由于显微镜图像尺寸较大且通常样本数量较少，因此为此目的使用cGAN具有挑战性。 当前的工作表明，该框架能够可靠地从胼胝体的MRI扫描中合成组织学图像，强调了网络在高分辨率组织学图像与相对较低分辨率MRI扫描配对时的训练能力。 以避免活检为最终目标，所提出的工具可以用于教育目的。