Stimulated Raman scattering (SRS) microscopy is a powerful chemical imaging technique that acquires images based on the vibrational-spectral "fingerprints" of molecules within an imaged field of view often without the need for exogenous fluorophores or labels. SRS microscopy has found an established niche in biophotonics with many examples of translational clinical applications and demonstrations of imaging various biological systems on subcellular to tissue spatial orders. Concurrent to the development of SRS microscopy, computational advancements have seen a democratized adoption of deep learning platforms for a wide variety of computer vision tasks. In this work I document my contributions in integrating SRS microscopy and deep learning towards advancing the capability to study biological systems. Specifically, deep learning will be shown to address technical limitations of SRS microscopy such as imaging noise and ultimate imaging depth in tissue samples. Deep learning will also be shown to improve analysis of SRS images via the development of a novel convolutional neural network architecture designed to handle a variety of chemical imaging techniques and perform a variety of computer vision tasks. Finally, I will show how these advancements and novel architecture can be used to diagnose thyroid cancer in label-free human tissue samples and to classify and study T cells based on label-free images.

Stimulated Raman Scattering Microscopy: Techniques and Applications describes innovations in instrumentation, data science, chemical probe development, and various applications enabled by a state-of-the-art stimulated Raman scattering (SRS) microscope. Beginning by introducing the history of SRS, this book is composed of seven parts in depth including instrumentation strategies that have pushed the physical limits of SRS microscopy, vibrational probes (which increased the SRS imaging functionality), data science methods, and recent efforts in miniaturization. This rapidly growing field needs a comprehensive resource that brings together the current knowledge on the topic, and this book does just that. Researchers who need to know the requirements for all aspects of the instrumentation as well as the requirements of different imaging applications (such as different types of biological tissue) will benefit enormously from the examples of successful demonstrations of SRS imaging in the book. Led by Editor-in-Chief Ji-Xin Cheng, a pioneer in coherent Raman scattering microscopy, the editorial team has brought together various experts on each aspect of SRS imaging from around the world to provide an authoritative guide to this increasingly important imaging technique. This book is a comprehensive reference for researchers, faculty, postdoctoral researchers, and engineers. - Includes every aspect from theoretic reviews of SRS spectroscopy to innovations in instrumentation and current applications of SRS microscopy - Provides copious visual elements that illustrate key information, such as SRS images of various biological samples and instrument diagrams and schematics - Edited by leading experts of SRS microscopy, with each chapter written by experts in their given topics

Advances in Nonlinear Photonics combines fundamental principles with an overview of the latest developments. The book is suitable for the multidisciplinary audience of photonics researchers and practitioners in academia and R&D, including materials scientists and engineers, applied physicists, chemists, etc. As nonlinear phenomena are at the core of photonic devices and may enable future applications such as all-optical switching, all-optical signal processing and quantum photonics, this book provides an overview of key concepts. In addition, the book reviews the most important advances in the field and how nonlinear processes may be exploited in different photonic applications. - Introduces fundamental principles of nonlinear phenomena and their application in materials and devices - Reviews and provides definitions of the latest research directions in the field of nonlinear photonics - Discusses the most important developments in materials and applications, including future prospects

Stimulated Raman scattering (SRS) microscopy has transformed our ability to image chemical and biological systems because of its sub-micrometer resolution and molecular specificity. However, limitations in small spectral coverage, poor spectral resolution, and low sensitivity hamper the ability of SRS to investigate complex systems. Furthermore, the usage of SRS microscopy beyond biological systems is still very limited. Here I present my work which specifically addresses these issues. In chapters 2 and 3 I demonstrate the benefits of using SRS microscopy outside of its current biological niche, by investigating materials such as latent fingerprints and pharmaceutical tablets. In chapter 4 I address the limitations of poor spectral resolution and small spectral coverage by developing a parabolic fiber amplifier to provide a better SRS microscope. In chapter 5 I demonstrate an application of the developed fiber amplifier system for monitoring and quantifying temperature at the microscale level. Lastly, in chapter 6 I introduce the development of a new optical parametric oscillator (OPO) source to address the final limitation by increasing the sensitivity of SRS imaging, which ties into the future outlook on advancements needed in SRS microscopy.

The First Book on CRS MicroscopyCompared to conventional Raman microscopy, coherent Raman scattering (CRS) allows label-free imaging of living cells and tissues at video rate by enhancing the weak Raman signal through nonlinear excitation. Edited by pioneers in the field and with contributions from a distinguished team of experts, Coherent Raman Sc

Coherent Raman microscopy (CRM) is a powerful nonlinear optical imaging technique based on contrast via Raman active molecular vibrations. CRM has been used in domains ranging from biology to medicine to geology in order to provide quick, sensitive, chemical-specific, and label-free 3D sectioning of samples. The Raman contrast is usually obtained by combining two ultrashort pulse input beams, known as Pump and Stokes, whose frequency difference is adjusted to the Raman vibrational frequency of interest. CRM can be used in conjunction with other imaging modalities such as second harmonic generation, fluorescence, and third harmonic generation microscopy, resulting in a multimodal imaging technique that can capture a massive amount of data. Two fundamental elements are crucial in CRM. First, a laser source which is broadband, stable, rapidly tunable, and low in noise. Second, a strategy for image analysis that can handle denoising and material classification issues in the relatively large datasets obtained by CRM techniques. Stimulated Raman Scattering (SRS) microscopy is a subset of CRM techniques, and this thesis is devoted entirely to it. Although Raman imaging based on a single vibrational resonance can be useful, non-resonant background signals and overlapping bands in SRS can impair contrast and chemical specificity. Tuning over the Raman spectrum is therefore crucial for target identification, which necessitates the use of a broadband and easily tunable laser source. Although supercontinuum generation in a nonlinear fibre could provide extended tunability, it is typically not viable for some CRM techniques, specifically in SRS microscopy. Signal acquisition schemes in SRS microscopy are focused primarily on detecting a tiny modulation transfer between the Pump and Stokes input laser beams. As a result, very low noise source is required. The primary and most important component in hyperspectral SRS microscopy is a low-noise broadband laser source. The second problem in SRS microscopy is poor signal-to-noise (SNR) ratios in some situations, which can be caused by low target-molecule concentrations in the sample and/or scattering losses in deep-tissue imaging, as examples. Furthermore, in some SRS imaging applications (e.g., in vivo), fast imaging, low input laser power or short integration time is required to prevent sample photodamage, typically resulting in low contrast (low SNR) images. Low SNR images also typically suffer from poorly resolved spectral features. Various de-noising techniques have been used to date in image improvement. However, to enable averaging, these often require either previous knowledge of the noise source or numerous images of the same field of view (under better observing conditions), which may result in the image having lower spatial-spectral resolution. Sample segmentation or converting a 2D hyperspectral image to a chemical concentration map, is also a critical issue in SRS microscopy. Raman vibrational bands in heterogeneous samples are likely to overlap, necessitating the use of chemometrics to separate and segment them. We will address the aforementioned issues in SRS microscopy in this thesis. To begin, we demonstrate that a supercontinuum light source based on all normal dispersion (ANDi) fibres generates a stable broadband output with very low incremental source noise. The ANDi fibre output's noise power spectral density was evaluated, and its applicability in hyperspectral SRS microscopy applications was shown. This demonstrates the potential of ANDi fibre sources for broadband SRS imaging as well as their ease of implementation. Second, we demonstrate a deep learning neural net model and unsupervised machine-learning algorithm for rapid and automated de-noising and segmentation of SRS images based on a ten-layer convolutional autoencoder: UHRED (Unsupervised Hyperspectral Resolution Enhancement and De-noising). UHRED is trained in an unsupervised manner using only a single ("one-shot") hyperspectral image, with no requirements for training on high quality (ground truth) labelled data sets or images.