论文标题：
This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model
作者：
Alon Saguy, Tav Nahimov, Maia Lehrman, Estibaliz Gómez-de-Mariscal, Iván Hidalgo-Cenalmor, Onit Alalouf, Ashwin Balakrishnan, Mike Heilemann, Ricardo Henriques, Yoav Shechtman
期刊：
Small Methods
发表时间：
2024/10/14
数字识别码：
10.1002/smtd.202400672
摘要：
Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.
摘要翻译
（由计算机程序完成，仅供参考，内容以英文原文为准）：
生成模型，如扩散模型，近年来取得了重大进展，能够在各个领域合成高质量的真实数据。本文探讨了超分辨率显微图像扩散模型的适应和训练。结果表明，生成的图像与实验图像相似，并且生成过程没有表现出对训练集中现有图像的大量记忆。为了证明生成模型对数据增强的有用性，将使用生成的高分辨率数据训练的基于深度学习的单图像超分辨率（SISR）方法的性能与单独使用实验图像或数学建模生成的图像进行训练进行了比较。通过使用一些实验图像，重建图像的重建质量和空间分辨率得到了提高，展示了扩散模型图像生成在克服显微镜图像收集和注释的局限性方面的潜力。最后，该管道公开可用，可在线运行，用户友好，使研究人员能够生成自己的合成显微镜数据。这项工作展示了生成扩散模型对显微镜任务的潜在贡献，并为它们在该领域的未来应用铺平了道路。
所属学科：
人工智能
