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.
Large DOF (depth-of-field) with high SNR (signal-noise-ratio) imaging plays an important role in many applications such as unmanned driving to medical imaging. However, there is always a trade-off between DOF and SNR in traditional optical design. In this paper, we propose a NIR&VISCAM (NIR&VIS Camera) that combines multi-spectral optical design and deep learning to realize large DOF and high SNR imaging. Specifically, a multi-spectral optical imaging system based on the HVS (human visual system) is designed to provide colorful but small DOF VIS (visible) image and large DOF NIR (near-infrared) image. To achieve DOF extension, we build a fusion network NIR&VISNet consisting of a VIS encoder for color extraction, a NIR encoder for spatial details extraction and a decoder for information fusion. We establish a prototype to capture real-scene dataset containing 1000 sets and test our method on a variety of test samples. The experimental results demonstrate that our NIR&VISCAM can effectively produce large DOF images with high quality. Moreover, compared to the classic image fusion methods, our designed algorithm achieves the optimal performance in DOF extension and color fidelity. With the prominent performance in large DOF and high SNR imaging, this novel and portable system is promising for vision applications such as smartphone photography, industry detection, and life medical.
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