Quantification of collagen fiber structure using second harmonic generation imaging and two‐dimensional discrete Fourier transform analysis: Application to the human optic nerve head

Second harmonic generation (SHG) microscopy is widely used to image collagen fiber microarchitecture due to its high spatial resolution, optical sectioning capabilities and relatively nondestructive sample preparation. Quantification of SHG images requires sensitive methods to capture fiber alignment. This article presents a two‐dimensional discrete Fourier transform (DFT)–based method for collagen fiber structure analysis from SHG images. The method includes integrated periodicity plus smooth image decomposition for correction of DFT edge discontinuity artefact, avoiding the loss of peripheral image data encountered with more commonly used windowing methods. Outputted parameters are as follows: the collagen fiber orientation distribution, aligned collagen content and the degree of collagen fiber dispersion along the principal orientation. We demonstrate its application to determine collagen microstructure in the human optic nerve head, showing its capability to accurately capture characteristic structural features including radial fiber alignment in the innermost layers of the bounding sclera and a circumferential collagen ring in the mid‐stromal tissue. Higher spatial resolution rendering of individual lamina cribrosa beams within the nerve head is also demonstrated. Validation of the method is provided in the form of correlative results from wide‐angle X‐ray scattering and application of the presented method to other fibrous tissues.      

Spatial orientation mapping of fibers using polarization‐sensitive second harmonic generation microscopy

In this work, we present a non‐invasive approach to determine azimuth and elevation angles of collagen fibers capable of generating second harmonic signal. The azimuth angle was determined using the minimum of second harmonic generation (SHG) signal while rotating the plane of polarization of excitation light. The elevation angle was estimated from the ratio of the minimal SHG intensity to the intensity when laser polarization and fiber directions were parallel to each other using experimentally determined calibration curve. Pixel‐resolution images of collagen fiber spatial orientation in tendon from bovine leg, chicken leg, and chicken skin were acquired using our approach of SHG polarization‐resolved microscopy. (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Analyzing the feasibility of discriminating between collagen types I and II using polarization‐resolved second harmonic generation

According to previous studies, the nonlinear susceptibility tensor ratio χ₃₃/χ₃₁ obtained from polarization‐resolved second harmonic generation (P‐SHG) under the assumption of cylindrical symmetry can be used to distinguish between fibrillar collagen types. Discriminating between collagen fibrils of types I and II is important in tissue engineering of cartilage. However, cartilage has a random organization of collagen fibrils, and the assumption of cylindrical symmetry may be incorrect. In this study, we simulated the P‐SHG response from different collagen organizations and demonstrated a possible method to exclude areas where cylindrical symmetry is not fulfilled and where fibrils are located in the imaging plane. The χ₃₃/χ₃₁‐ratio for collagen type I in tendon and collagen type II in cartilage was estimated to be 1.33 and 1.36, respectively, using this method. These ratios are now much closer than what has been reported previously in the literature, and the larger reported differences between collagen types can be explained by variation in the structural organization.      

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree‐based pipeline optimization tool

Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.     

Applications of second harmonic generation (SHG)/sum-frequency generation (SFG) imaging for biophysical characterization of the plasma membrane

The plasma membrane is a lipid bilayer of < 10 nm width that separates intra- and extra-cellular environments and serves as the site of cell-cell communication, as well as communication between cells and the extracellular environment. As such, biophysical phenomena at and around the plasma membrane play key roles in determining cellular physiology and pathophysiology. Thus, the selective visualization and characterization of the plasma membrane are crucial aspects of research in wide areas of biology and medicine. However, the specific characterization of the plasma membrane has been a challenge using conventional imaging techniques, which are unable to effectively distinguish between signals arising from the plasma membrane and those from intracellular lipid structures. In this regard, interface-specific second harmonic generation (SHG) and sum-frequency generation (SFG) imaging demonstrate great potential. When combined with exogenous SHG/SFG active dyes, SHG/SFG can specifically highlight the plasma membrane as the most prominent interface associated with cells. Furthermore, SHG/SFG imaging can be readily extended to multimodal multiphoton microscopy with simultaneous occurrence of other multiphoton phenomena, including multiphoton excitation and coherent Raman scattering, which shed light on the biophysical properties of the plasma membrane from different perspectives. Here, we review traditional and current applications, as well as the prospects of long-known but unexplored SHG/SFG imaging techniques in biophysics, with special focus on their use in the biophysical characterization of the plasma membrane.     

Automatic and label‐free identification of blood vessels in gliomas using the combination of multiphoton microscopy and image analysis

Currently, the targeted treatment of tumor based on the tumor microenvironment is newly developed. Blood vessels are the key parts in the tumor microenvironment, which is taken as a new visible target for tumor therapy. Multiphoton microscopy (MPM), based on the second harmonic generation and two‐photon excited fluorescence, is available to make the label‐free analysis on the blood vessels in human gliomas. MPM can reveal the vascular morphological characteristics in gliomas, including vascular malformation, intense vascular proliferation, perivascular collagen deposition, perivascular lymphocytes aggregation and microvascular proliferation. In addition, the image analysis algorithms were developed to automatically calculate the perivascular collagen content, vascular cavity area, lumen area, wall area and vessel number. Thus, the vascular morphology, the perivascular collagen deposition and intense vascular proliferation degree can be further quantitatively characterized. Compared with the pathological analysis, the combination of MPM and image analysis has potential advantages in making a quantitative and qualitative analyzing on vascular morphology in glioma microenvironment. As micro‐endoscope and two‐photon fiberscope are technologically improved, this combined method will be a useful imaging way to make the real‐time research on the targeting tumor microenvironment in gliomas.     

On‐the‐fly detection of changes on and below the surface in epithelium mucosal tissue architecture from scattered light

In this paper we present a technique to raise a flag on the fly when a transition occurs between different mucosal architectures on or below the surface. The segmentation is based on a novel difference metric for detecting an abrupt change in the parameters extracted from a Stochastic Decomposition Method (SDM) that models the scattered light reflected from the mucosal tissue structure over an area (2‐D scan) illuminated by an optical sensor (fiber) emitting light at either one wavelength or with white light. This work has the potential to enhance the endoscopist's ability to locate and identify abnormal mucosal architectures in particular when the disease is developing below the surface and hence becoming hidden during colonoscopy or endoscopic examination. It also has also potential in helping deciding as to when and where to take biopsies; steps that should lead to improvement in the diagnostic yield. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)