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.
Light exerts non-visual effects on a wide range of biological functions and behavior apart from the visual effect. Light can regulate human circadian rhythms, like the secretion of melatonin and cortisol. Light also has influence on body’s physiological parameters, such as blood pressure, heart rate and body temperature. However, human cognitive performance, alertness and mood under different lighting conditions have not been considered thoroughly especially for the complicated visual task like surgical operating procedure. In this paper, an experiment was conducted to investigate the cognition, alertness and mood of healthy participants in a simulated operating room (OR) in the hospital. A LED surgical lamp was used as the light source, which is mixed by three color LEDs (amber, green and blue). The surgical lamp is flexible on both spectrum and intensity. Exposed to different light settings, which are varied from color temperature and luminance, participants were asked to take psychomotor vigilance task (PVT) for alertness measurement, alphabet test for cognitive performance measurement, positive and negative affect schedule (PANAS) for mood measurement. The result showed the participants’ cognitive performance, alertness and mood are related to the color temperature and luminance of the LED light. This research will have a guidance for the surgical lighting environment, which can not only enhance doctors’ efficiency during the operations, but also create a positive and peaceful surgical lighting environment.
Surgical light is important for helping the surgeon easily identify specific tissues during an operation. We propose a spectral reflectance comparison model to optimize the light-emitting diode light spectrum in the operating room. An entropy evaluation method, meant specifically for surgical situations, was developed to evaluate images of biological samples. White light was mixed to achieve an optimal spectrum, and images of different tissues under the light were captured and analyzed. Results showed that images obtained with light with an optimal spectrum had a higher contrast than those obtained with a commercial white light of different color temperatures. Optimized surgical light obtained using this simple and effective method could replace the traditional surgical illumination systems.
Light-emitting diode (LED) is the neotype surgical lighting device as an inexpensive and color-variable illumination. A methodology was designed to value the quality of surgical lighting and used to develop an operation lamp with LEDs enhancing the biological contrast. We assembled a modular array of Phillips LEDs as illumination. In the initial experiment, images of porcine heart were carried out in several LED environments and analyzed quantitatively to assess the function of these LEDs in contrast enhancement. Then we measured the reflectance spectrums of blood, fat and other tissues to obtain the spectral comparison. Based on the result, new illuminations with spectral components which differ most in the comparison was developed. Meanwhile, a new evaluation function combining the entropy analysis and brightness contrast was also built to value the quality of these illuminations. Experiments showed biological features are more visible with treated LED illuminations than the broadband lamps. Thus, the synthesis of LED lighting spectra could be adjusted to provide significant tissue identification. Therefore, we believe the new methodology will contribute to the manufacture of high efficient medical illuminations and act the positive role in coming surgical lighting fields.
A design method for the distortionless catadioptric panoramic imaging system is proposed in this paper. The panoramic
system mainly consists of two parts, a reflecting surface system with relay lens and a CCD camera. A mapping
relationship between the real image plane and the projection surface is established to acquires low distorted imaging
features easily. And the design of freeform surface is applied to the reflecting surfaces to correct distortion. After
iteratively optimize the freeform surfaces, the image quality is gradually improved. The simulation results show that
compared with traditional system, the new freeform surface system has simple design, attaining higher performance and
has the advantage of small scene distortion and making the image more suitable and convenient for observing.
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