Mr. Jiaqi Li Profile

Jiaqi Li

at Sichuan Univ

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Proceedings Article | 23 November 2024 Poster + Paper

Deep learned virtual staining for highly accurate tissue examination

Chaoqiang Wu, Yushan Guo, Mingwei Zhou, Tingdong Kou, Jiaqi Li, Wenyi Jing, Junfei Shen

Proceedings Volume 13239, 1323915 (2024) https://doi.org/10.1117/12.3036182

KEYWORDS: Multispectral imaging, RGB color model, Tissues, Image restoration, Visualization, Cameras, Optical imaging, Imaging systems, Image processing

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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.

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