在小样本量设置下通过生成对抗网络实现显微镜与磁共振成像之间的风格迁移
Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings
AI总结：
提出基于cGAN的框架，在小样本量下实现从人类胼胝体MRI扫描生成显微组织学图像的跨模态转换，可靠合成高分辨率组织学图像，为避免活检的教育目的提供新工具。
摘要：
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广阔前景，因为它可以在没有潜在侵入性活检程序的情况下进行组织病理学分析。在此，我们测试了一种使用条件生成对抗网络（cGAN）架构从人类胼胝体MRI扫描生成显微组织学图像的方法。据我们所知，这是首次将大脑MRI进行多模态转换为同一样本的组织学体积表示。该技术通过训练配对图像转换模型进行评估，该模型接收来自MRI扫描和显微镜的图像集。将cGAN用于此目的具有挑战性，因为显微镜图像尺寸大且通常样本可用性低。当前工作表明，该框架能够可靠地从胼胝体MRI扫描合成组织学图像，突显了网络在配对相对较低分辨率MRI扫描的高分辨率组织学上进行训练的能力。以避免活检为最终目标，所提出的工具可用于教育目的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
