Deep learning-based inpainting of saturation artifacts in optical coherence tomography images  

作　　者：Muyun Hu Zhuoqun Yuan Di Yang Jingzhu Zhao Yanmei Liang 

机构地区：[1]Institute of Modern Optics,Nankai University,Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology,Tianjin 300350,China [2]Department of Thyroid and Neck Tumor,Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer,Key Laboratory of Cancer Prevention and Therapy,Tianjin 300060,China

出　　处：《Journal of Innovative Optical Health Sciences》2024年第3期1-10,共10页创新光学健康科学杂志（英文）

基　　金：supported by the National Natural Science Foundation of China(62375144 and 61875092);Tianjin Foundation of Natural Science(21JCYBJC00260);Beijing-Tianjin-Hebei Basic Research Cooperation Special Program(19JCZDJC65300).

摘　　要：Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

关 键 词：Optical coherence tomography saturation artifacts deep learning image inpainting. 

分 类 号：O43[机械工程—光学工程] TP391.41[理学—光学]