Unsupervised content-preserving transformation for optical microscopy

• 作者: Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai
• 来源期刊: 光：科学与应用（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Articles
• 页码范围: 390-400
• 页数: 11页
• 语种: 英文

摘要

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.