High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering.

Published on Sep 11, 2020in Journal of Physical Chemistry Letters6.71
· DOI :10.1021/ACS.JPCLETT.0C01598
Jing Zhang1
Estimated H-index: 1
,
Jian Zhao10
Estimated H-index: 10
(BU: Boston University)
+ 2 AuthorsJi-Xin Cheng83
Estimated H-index: 83
Sources
Abstract
Hyperspectral stimulated Raman scattering (SRS) by spectral focusing can generate label-free chemical images through temporal scanning of chirped femtosecond pulses. Yet, pulse chirping decreases t...
📖 Papers frequently viewed together
1996
1 Author (P. M. Mikheev)
53 Citations
References0
Newest
#1Kideog Bae (NUS: National University of Singapore)H-Index: 5
#2Wei Zheng (NUS: National University of Singapore)H-Index: 170
Last. Zhiwei Huang (NUS: National University of Singapore)H-Index: 47
view all 3 authors...
High speed imaging is pre-requisite for monitoring of dynamic processes in biological events. Here we report the development of a unique spatial light-modulated stimulated Raman scattering (SLM-SRS) microscopy that tailors the broadband excitation beam with sparse-sampling masks designed for rapid multiplexed vibrational imaging to monitor real-time cancer treatment effects and in vivo transport of drug solvent. Methods: We design an optimal mask pattern that enables selection of predominant win...
5 CitationsSource
#1Jian Zhao (UCF: University of Central Florida)H-Index: 27
#1Jian Zhao (UCF: University of Central Florida)H-Index: 10
Last. Axel Schülzgen (UCF: University of Central Florida)H-Index: 35
view all 8 authors...
We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber. The cell samples are illuminated by an incoherent LED light source. A deep convolutional neural network is applied to the image reconstruction process. The network training uses data generated by a setup with straight fiber at room temperature (∼20 ° C) but can be utilized directly for high-fidelity reconstruction of cell images t...
2 CitationsSource
#1Bryce Manifold (UW: University of Washington)H-Index: 4
#2Elena C. Thomas (UW: University of Washington)H-Index: 2
Last. Dan Fu (UW: University of Washington)H-Index: 24
view all 5 authors...
Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging technique that has demonstrated great utility in biomedical imaging applications ranging from real-time stain-free histopathology to live animal imaging. However, similar to many other nonlinear optical imaging techniques, SRS images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light in tissue as well as the limitation in applicable power to minimize photodamage. We...
22 CitationsSource
#1Yair Rivenson (UCLA: University of California, Los Angeles)H-Index: 34
#2Hongda Wang (UCLA: University of California, Los Angeles)H-Index: 14
Last. Aydogan OzcanH-Index: 79
view all 13 authors...
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual sta...
135 CitationsSource
#1Chawin Ounkomol (Allen Institute for Cell Science)H-Index: 3
#2Sharmishtaa Seshamani (Allen Institute for Brain Science)H-Index: 11
Last. Gregory R. Johnson (Allen Institute for Cell Science)H-Index: 8
view all 5 authors...
Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extend...
159 CitationsSource
#1Chi Zhang (BU: Boston University)H-Index: 57
#1Chi Zhang (BU: Boston University)H-Index: 20
Last. Ji-Xin Cheng (BU: Boston University)H-Index: 83
view all 2 authors...
Chemical imaging offers critical information to understand the fundamentals in biology and to assist clinical diagnostics. Label-free chemical imaging piques a general interest since it avoids the use of bio-perturbing molecular labels and holds promises to characterize human tissue in vivo. Coherent Raman scattering (CRS), which utilizes lasers to excite the vibrations of molecules, renders new modalities to map chemicals in living samples without the need of labeling and provides significantly...
17 CitationsSource
#1Elias Nehme (Technion – Israel Institute of Technology)H-Index: 8
#2Lucien E. Weiss (Technion – Israel Institute of Technology)H-Index: 14
Last. Yoav Shechtman (Technion – Israel Institute of Technology)H-Index: 24
view all 4 authors...
We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and ...
232 CitationsSource
#1Haonan Lin (BU: Boston University)H-Index: 6
#2Chien-Sheng Liao (BU: Boston University)H-Index: 13
Last. Ji-Xin Cheng (BU: Boston University)H-Index: 83
view all 5 authors...
Increasing image acquisition speed of a spectroscopic imaging technique opens the door for real-time detection of molecules in living cells. Spectroscopic stimulated Raman scattering imaging is a nondestructive, label-free technique for detecting the chemical fingerprints of biological molecules. However, its relatively slow image acquisition speed has limited its use. This led Ji-Xin Cheng and colleagues from Boston University and Purdue University in the United States to develop a method that ...
24 CitationsSource
#1Yair Rivenson (UCLA: University of California, Los Angeles)H-Index: 34
#2Zoltán Göröcs (UCLA: University of California, Los Angeles)H-Index: 16
Last. Aydogan Ozcan (UCLA: University of California, Los Angeles)H-Index: 79
view all 6 authors...
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better...
309 CitationsSource
Jul 21, 2017 in CVPR (Computer Vision and Pattern Recognition)
#1Simon JégouH-Index: 6
#2Michal Drozdzal ('ENS Paris': École Normale Supérieure)H-Index: 17
Last. Yoshua BengioH-Index: 192
view all 5 authors...
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.,,,,,, Recently, a new CNN architecture,...
599 CitationsSource
Cited By4
Newest
#1Tao Chen (BCM: Baylor College of Medicine)
#2Ahmet Yavuz (BCM: Baylor College of Medicine)H-Index: 1
Last. Meng C. Wang (BCM: Baylor College of Medicine)H-Index: 24
view all 3 authors...
Lipid droplets (LDs) are lipid-rich organelles universally found in most cells. They serve as a key energy reservoir, actively participate in signal transduction and dynamically communicate with other organelles. LD dysfunction has been associated with a variety of diseases. The content level, composition and mobility of LDs are crucial for their physiological and pathological functions, and these different parameters of LDs are subject to regulation by genetic factors and environmental inputs. ...
Source
#1Pedram AbdolghaderH-Index: 1
#2Andrew RidsdaleH-Index: 17
Last. Isaac TamblynH-Index: 19
view all 7 authors...
Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful label-free, chemical-specific technique for biomedical and mineralogical imaging which can suffer from low signal-to-noise ratios due to requirements of low input laser power or fast imaging, or from optical scattering and low target concentration. Here, we demonstrate a deep learning neural net model and unsupervised machine-learning algorithm for rapid and automatic de-noising and segmentation of SRS images based on a ten...
#1Bryce Manifold (UW: University of Washington)H-Index: 4
#2Shuaiqian Men (UW: University of Washington)H-Index: 2
Last. Dan Fu (UW: University of Washington)H-Index: 24
view all 4 authors...
Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or colour imaging based on the reflection, transmission or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here we present a new flexible architecture—the U-within-U-Net—that can perform classification, segmentation and prediction of orthogonal imaging...
1 CitationsSource
#1Hao He (Ha Tai: Xiamen University)H-Index: 2
#2Sen Yan (Ha Tai: Xiamen University)H-Index: 2
Last. Bin Ren (Ha Tai: Xiamen University)H-Index: 80
view all 9 authors...
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging ...
Source
#1Chenxi Qian (California Institute of Technology)H-Index: 16
#2Kun Miao (California Institute of Technology)H-Index: 8
Last. Lu Wei (California Institute of Technology)H-Index: 19
view all 6 authors...
Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA...
1 CitationsSource