Abstract:A coherent anti-Stokes Raman scattering (CARS) rigid endoscope was developed to visualize peripheral nerves without labeling for nerve-sparing endoscopic surgery. The developed CARS endoscope had a problem with low imaging speed, i.e. low imaging rate. In this study, we demonstrate that noise reduction with deep learning boosts the nerve imaging speed with CARS endoscopy. We employ fine-tuning and ensemble learning and compare deep learning models with three different architectures. In the fine-tuning strategy… Show more
“…Raman microscopy is another option for label-free imaging; however, because of the poor effectiveness of Raman scattering, neuron imaging with ordinary spontaneous Raman scattering needs a considerable exposure period [ 237 ]. Plasmonic materials have been employed to boost the Raman technique’s sensitivity.…”
The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.
“…Raman microscopy is another option for label-free imaging; however, because of the poor effectiveness of Raman scattering, neuron imaging with ordinary spontaneous Raman scattering needs a considerable exposure period [ 237 ]. Plasmonic materials have been employed to boost the Raman technique’s sensitivity.…”
The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.
“…During the training phase, the deep learning model adjusts its inner weights so that the MSE is reduced towards zero. While this loss function has been extensively used in regression and denoising tasks, recent studies suggest adding the structured similarity index (SSIM) to enhance the quality of the recovered signal [13,14]. One point to consider in the Raman denoising problem is that the dominant peaks in the signal are of high importance in analyzing the material structure.…”
Section: The Custom Loss Functionmentioning
confidence: 99%
“…In recent years, deep learning Convolutional Neural Networks (CNNs) have been shown to perform well at denoising coherent Raman spectra when compared to established denoising techniques [12][13][14]. In these contributions deep learning was applied to threedimensional hyperspectral image sets.…”
Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.
“…Then a CARSM with imaging speed of video-rate was developed for vibrational imaging of tissues in vivo by Xie's group via detecting strong backscattering of forward CARS signal with video-rate scanning microscopy [15]. Today, higher speed imaging has been achieved by employing more techniques, such as Fourier-transform [16] and deep-learning noise reduction [17]. The spectral bandwidth of CARSM has also shown great increase by multiplex [18,19] and broadband CARSM techniques [20,21].…”
By eliminating the photodamage and photobleaching induced by high intensity laser and fluorescent molecular, the label-free laser scanning microscopy shows powerful capability for imaging and dynamic tracing to biological tissues and cells. In this review, three types of label-free laser scanning microscopies: laser scanning coherent Raman scattering microscopy, second harmonic generation microscopy and scanning localized surface plasmon microscopy are discussed with their fundamentals, features and recent progress. The applications of label-free biological imaging of these laser scanning microscopies are also introduced. Finally, the performance of the microscopies is compared and the limitation and perspectives are summarized.
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