Deep learning virtual colorful lens-free on-chip microscopy

• 来源期刊: 中国光学快报（英文版）
• 年，卷（期）: 2020,（12）
• 所属期刊栏目: Biophotonics
• 页码范围: 54-58
• 页数: 5页
• 语种: 英文
• DOI: 10.3788/COL202018.121705

摘要

Currently,it is generally known that lens-free holographic microscopy,which has no imaging lens,can realize a large field-of-view imaging with a low-cost setup.However,in order to obtain colorful images,traditional lens-free holographic microscopy should utilize at least three quasi-chromatic light sources of discrete wavelengths,such as red LED,green LED,and blue LED.Here,we present a virtual colorization by deep learning methods to transfer a gray lens-free microscopy image into a colorful image.Through pairs of images,i.e.,grayscale lens-free microscopy images under green LED at 550 nm illumination and colorful bright-field microscopy images,a gen-erative adversarial network(GAN)is trained,and its effectiveness of virtual colorization is proved by applying it to hematoxylin and eosin stained pathological tissue samples imaging.Our computational virtual colorization method might strengthen the monochromatic illumination lens-free microscopy in medical pathology applica-tions and label staining biomedical research.