2021/10/14 by Bowen Li, Li, Bowen, Shiyu Tan +11 · 1 citation
Engineering · Medicine · Physics and Astronomy · #Artificial intelligence #Biological Physics (physics.bio-ph) #Computer science #Computer vision #Confocal #Confocal microscopy #FOS: Physical sciences #Materials science #Microscope #Microscopy #Optical Coherence Tomography Applications #Optical Imaging and Spectroscopy Techniques #Optical sectioning #Optics #Optics (physics.optics) #Photoacoustic and Ultrasonic Imaging #Physics #Resolution (logic) #physics.bio-ph #physics.optics
paper · pdf · doi:10.48550/arxiv.2110.07218
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/10/14 · openalex publication_date 2021/10/14 · arxiv updated 2021/10/15 · openalex created_date 2022/11/16 · openalex updated_date 2026/07/28
Confocal microscopy is the standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in a confocal leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to "teach" a traditional wide-field microscope, one that's available in every lab, into producing 3D volumetric images like a confocal. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D Generative Adversarial Network (GAN) based neural network to learn the mapping between the blurry low-contrast image stack obtained using wide-field and the sharp, high-contrast images obtained using a confocal. After training the network with widefield-confocal image pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick (∼40 microns) highly-scattering samples. We believe that such learning-based-microscopes have the potential to bring confocal quality imaging to every lab that has a wide-field microscope.