3D high resolution generative deep-learning network for fluorescence microscopy imaging

Zhou, Hang; Cai, Ruiyao; Quan, Tingwei; Liu, Shijie; Li, Shiwei; Huang, Qing; Ertuerk, Ali and Zeng, Shaoqun (2020): 3D high resolution generative deep-learning network for fluorescence microscopy imaging. In: Optics Letters, Vol. 45, No. 7: pp. 1695-1698

Abstract

Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover 20 x /1.0-NAvolume images from coarsely registered 5 x /0.16-NA volume images collected by light-sheet microscopy. This method. would provide great potential in applications which require high resolution volume imaging. (C) 2020 Optical Society of America

• Item Type: Journal article
• Faculties: Medicine; Medicine > Munich Cluster for Systems Neurology (SyNergy)
• Subjects: 600 Technology > 610 Medicine and health
• ISSN: 0146-9592
• Language: English
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198