标题： 基于通用预训练模型的双向脑图像翻译使用迁移学习 显示英文标题

标题： Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models

摘要： 脑成像在诊断和治疗各种神经疾病方面发挥着关键作用，提供了关于大脑结构和功能的有价值见解。 诸如磁共振成像（MRI）和计算机断层扫描（CT）等技术能够实现无创的大脑可视化，有助于理解大脑解剖结构、异常和功能连接。 然而，成本和辐射剂量可能会限制特定图像模式的获取，因此可以使用医学影像合成来生成所需的医学图像而无需实际添加。 在医学领域，获得标记的医学图像是劳动密集且昂贵的，解决数据稀缺是一个主要挑战。 近期研究提出使用迁移学习来克服这一问题。 这涉及调整预先训练的CycleGAN模型，这些模型最初是在非医学数据上训练的，以生成逼真的医学图像。 在这项工作中，迁移学习被应用于MR-CT图像转换任务以及相反方向的任务，并使用了18个预先训练的非医学模型，模型经过微调以获得最佳效果。 通过四个广泛使用的图像质量指标评估了模型的性能： 峰值信噪比、结构相似性指数、通用质量 指数和视觉信息保真度。 放射科医生的定量评价和定性感知分析证明了迁移学习在医学影像中的潜力以及通用预训练模型的有效性。 结果提供了令人信服的证据，表明该模型表现出色，这可以归因于训练图像与实际人脑图像的高度质量和相似性。 这些结果强调了仔细选择适当且具有代表性的训练图像以优化大脑图像分析任务性能的重要性。 显示英文摘要

摘要： Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models' performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model's exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks.