Nonlinear optical microscopy in decoding arterial diseases

Development of an image classification pipeline for atherosclerotic plaques assessment using supervised machine learning

Background

During atherosclerosis, the narrowing of the arterial lumen is observed through the accumulation of bio compounds and the formation of plaque within artery walls. A non-linear optical imaging modality (NLOM), coherent anti-stokes Raman scattering (CARS) microscopy, can be used to image lipid-rich structures commonly found in atherosclerotic plaques. By matching the lipid’s molecular vibrational frequencies (CH bonds), it is possible to map the accumulation of lipid-rich structures without the need for exogenous labelling and/or processing of the samples. CARS allows for the visualization of the morphological features of plaque. In combination with supervised machine learning, CARS imaged morphological features can be used to characterize the progression of atherosclerotic plaques. 

Results

Based on a set of label-free CARS images of atherosclerotic plaques (i.e. foam cell clusters) from a Watanabe heritable hyperlipidemic rabbit model, we developed an automated pipeline to classify atherosclerotic lesions based on their major morphological features. Our method uses image preprocessing to first improve the quality of the CARS-imaged plaque, followed by the segmentation of the plaque using Otsu thresholding, marker-controlled watershed, K-means segmentation and a novel independent foam cell thresholding segmentation. To define relevant morphological features, 27 quantitative features were extracted and further refined by a novel coefficient of variation feature refinement method in accordance with filter-type feature selection. Refined morphological features were supplied into three supervised machine learning algorithms; K-nearest neighbour, support vector machine and decision tree classifier. The classification pipeline showcased the ability to exploit relevant plaque morphological features to accurately classify 3 pre-defined stages of atherosclerosis: early fatty streak development (EFS) and advancing atheroma (AA) with a greater than 85% class accuracy

Conclusions

Through the combination of CARS microscopy and computational methods, a powerful classification tool was developed to identify the progression of atherosclerotic plaque in an automated manner. Using a curated dataset, the classification pipeline demonstrated the ability to differentiate between EFS, EF and AA. Thus, presenting the opportunity to classify the onset of atherosclerosis at an earlier stage of development

Scattering of Light from the Systemic Circulatory System

There are many factors of methodological origin that influence the measurement of optical properties of the entire circulatory system which consists of blood as the basic component. The basic idea of this review article is to provide the optical properties of the circulatory system with all those factors of influence that have been employed in biomedical optics for different applications. We begin with the available optical properties, i.e., absorption, scattering and, reduced scattering coefficient, in general for any tissue inside the human body and prominent scattering theories (e.g., light, X-rays, neutrons) that are helpful in this regard. We have reviewed and compiled already available formulas and their respective available data for different human tissues for these optical properties. Then we have descended to the blood composition and to different scattering techniques available in the literature to study scattering and light propagation inside blood. We have reviewed both computational and theoretical scattering techniques.

Label-free multiphoton microscopy as a tool to investigate alterations of cerebral aneurysms

Cerebral aneurysms are abnormal focal dilatations of arterial vessel walls with pathological vessel structure alterations. Sudden rupture can lead to a subarachnoid hemorrhage, which is associated with a high mortality. Therefore, the origin of cerebral aneurysms as well as the progression to the point of rupture needs to be further investigated. Label-free multimodal multiphoton microscopy (MPM) was performed on resected human aneurysm domes and integrated three modalities: coherent anti-Stokes Raman scattering, endogenous two-photon fluorescence and second harmonic generation. We showed that MPM is a completely label-free and real-time powerful tool to detect pathognomonic histopathological changes in aneurysms, e.g. thickening and thinning of vessel walls, intimal hyperplasia, intra-wall haemorrhage, calcification as well as atherosclerotic changes. In particular, the loss or fragmentation of elastin as well as fibromatous wall remodelling appeared very distinct. Remarkably, cholesterol and lipid deposits were clearly visible in the multiphoton images. MPM provides morphological and biochemical information that are crucial for understanding the mechanisms of aneurysm formation and progression.

Multiphoton microscopy for pre-clinical evaluation of flow-diverter stents for treating aneurysms

BACKGROUND

Conventional histological analyses are the gold standard for the study of aneurysms and vascular pathologies in pre-clinical research. Over the past decade, in vivo and ex vivo imaging using multiphoton microscopy have emerged as powerful pre-clinical tools for detailed tissue analyses that can assess morphology, the extracellular matrix (ECM), cell density and vascularisation. Multiphoton microscopy allows for deeper tissue penetration with minor phototoxicity.

OBJECTIVE

The present study aimed to demonstrate the current status of multimodality imaging, including multiphoton microscopy, for detailed analyses of neo-endothelialisation and ECM evolution after flow-diverter stent (FDS) treatment in an experimental rabbit model of aneurysms.

METHODS

Multiphoton microscopy tools for assessing autofluorescence and second harmonic generation (SHG) signals from biological tissues were used to evaluate the endovascular treatment of intracranial aneurysms in an animal model of aneurysms (pig, rabbit). Results from multiphoton microscopy were compared to those from standard histology, electronic and bright field microscopy.

CONCLUSIONS

The present study describes novel evaluation modes based on multiphoton microscopy for visualising tissue morphology (e.g., collagen, elastin, and cells) to qualify and quantify the extent of neo-intimal formation of covered arteries and device integration into the arterial wall using a rabbit model of intracranial aneurysms treated with FDS.

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.

Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging

Structural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer. NEW & NOTEWORTHY Textural analyses such as fractal dimension (FD) and directional analysis (DA) are straightforward and computationally viable strategies to extract fiber-related morphological data from optical images. Therefore, objective, quantitative, and automated characterization of protein fiber morphology in extracellular matrix can be realized by using these methods in combination with digital imaging techniques such as nonlinear optical microscopy (NLOM), a highly effective visualization tool for fibrillar collagen and elastic network. Combining FD and DA with NLOM is an innovative approach to track alterations of structural fibrillar proteins. The results illustrated in this study not only prove the effectiveness of FD and DA methods in extracellular protein characterization but also demonstrate their potential value in clinical and basic biomedical research where protein microstructure characterization is critical.

Imaging Collagen in Scar Tissue: Developments in Second Harmonic Generation Microscopy for Biomedical Applications

The ability to respond to injury with tissue repair is a fundamental property of all multicellular organisms. The extracellular matrix (ECM), composed of fibrillar collagens as well as a number of other components is dis-regulated during repair in many organs. In many tissues, scaring results when the balance is lost between ECM synthesis and degradation. Investigating what disrupts this balance and what effect this can have on tissue function remains an active area of research. Recent advances in the imaging of fibrillar collagen using second harmonic generation (SHG) imaging have proven useful in enhancing our understanding of the supramolecular changes that occur during scar formation and disease progression. Here, we review the physical properties of SHG, and the current nonlinear optical microscopy imaging (NLOM) systems that are used for SHG imaging. We provide an extensive review of studies that have used SHG in skin, lung, cardiovascular, tendon and ligaments, and eye tissue to understand alterations in fibrillar collagens in scar tissue. Lastly, we review the current methods of image analysis that are used to extract important information about the role of fibrillar collagens in scar formation.

Collagen morphology and texture analysis: from statistics to classification

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage.