Deep learned virtual staining for highly accurate tissue examination

Chemical reagents are frequently used in traditional histological staining workflow bringing many drawbacks such as environment pollutions and health damages. Based on automated algorithm and computer computing power, virtual staining with non-pollution, strong robustness and high efficiency is proposed to refine it. However, traditional approaches still exist shortages on reliability due to unawareness of physical basis. In this paper, we fuse optical prior information into the virtual staining pipeline to realize highly robust staining. The total staining process is divided into three parts: (1) spectral staining, (2) optical imaging and (3) color correction. An end-to-end neural network oriented to visual staining is constructed for precise and automatic staining. Multi-scale convolution residual block (MultiResBlock) is designed to better handle with abundant information of spectral cubes while both channel attention and spatial attention modules are adopted to pay more attention to histopathological features. Experimental results demonstrate that generated stained images are visually equivalent with histologically stained. Our virtual staining method gives more robust results replying medical concerns of high reliability, and realizes full link co-optimization from front-end spectral staining to rear-end color correction. It is expected to play an important role in relieving the pressure of pathologist, achieving precision medicine and revealing the nature of life, etc.