Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation

Comparative study of OCTA algorithms with a high-sensitivity multi-contrast Jones matrix OCT system for human skin imaging

The multi-contrast Jones matrix OCT (JMT) system can not only improve the tissue-specific contrast but also enhance the sensitivity of detecting flow, which is gaining increasing attention. However, for the JMT system, there is currently a lack of studies evaluating and guiding the selection of suitable angiography algorithms to map the most appealing quality of angiograms for clinical use. In this paper, by a homemade high-sensitivity multi-contrast JMT system based 200 kHz swept source, the performance of two complex-signal-based OCTA methods that are insensitive to phase instability and one amplitude-signal-based OCTA method are compared for in-vivo imaging of human skin qualitatively and quantitatively. Six metrics, including vascular connectivity, image contrast-to-noise ratio, image signal-to-noise ratio, vessel diameter index, blood vessel density, and processing time, are assessed. The results show that the vascular networks processed by all OCTA methods and the texture of skin could be visualized simultaneously and markedly. Additionally, the complex-signal-based OCTA methods successfully suppress phase instabilities and even outperform the amplitude-signal-based OCTA algorithm in some indicators. This paper has a certain guiding significance for selecting an appropriate angiography algorithm and expanding the application field with this system.

Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation

We present a completely label-free three-dimensional (3D) optical coherence tomography (OCT)-based tissue dynamics imaging method for visualization and quantification of the metabolic and necrotic activities of tumor spheroid. Our method is based on a custom 3D scanning protocol that is designed to capture volumetric tissue dynamics tomography images only in a few tens of seconds. The method was applied to the evaluation of a tumor spheroid. The time-course viability alteration and anti-cancer drug response of the spheroid were visualized qualitatively and analyzed quantitatively. The similarity between the OCT-based dynamics images and fluorescence microscope images was also demonstrated.

Optical coherence tomography-based tissue dynamics imaging for longitudinal and drug response evaluation of tumor spheroids

We present optical coherence tomography (OCT)-based tissue dynamics imaging method to visualize and quantify tissue dynamics such as subcellular motion based on statistical analysis of rapid-time-sequence OCT signals at the same location. The analyses include logarithmic intensity variance (LIV) method and two types of OCT correlation decay speed analysis (OCDS). LIV is sensitive to the magnitude of the signal fluctuations, while OCDSs including early- and late-OCDS (OCDS e and OCDS l , respectively) are sensitive to the fast and slow tissue dynamics, respectively. These methods were able to visualize and quantify the longitudinal necrotic process of a human breast adenocarcinoma spheroid and its anti-cancer drug response. Additionally, the effects of the number of OCT signals and the total acquisition time on dynamics imaging are examined. Small number of OCT signals, e.g., five or nine suffice for dynamics imaging when the total acquisition time is suitably long.

Quantitative optical coherence tomography angiography: A review

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed.

Polarization-sensitive optical coherence elastography

Polarization-sensitive optical coherence elastography (PS-OCE) is developed for improved tissue discrimination. It integrates Jones matrix-based PS-optical coherence tomography (PS-OCT) with compression OCE. The method simultaneously measures the OCT intensity, attenuation coefficient, birefringence, and microstructural deformation (MSD) induced by tissue compression. Ex vivo porcine aorta and esophagus tissues were investigated by PS-OCE and histological imaging. The tissue properties measured by PS-OCE are shown as cross-sectional images and a three-dimensional (3-D) depth-trajectory plot. In this trajectory plot, the average attenuation coefficient, birefringence, and MSD were computed at each depth, and the trajectory in the depth direction was plotted in a 3-D feature space of these three properties. The tissue boundaries in a histological image corresponded with the depth-trajectory inflection points. Histogram analysis and t-distributed stochastic neighbour embedding (t-SNE) visualization of the three tissue properties indicated that the PS-OCE measurements provide sufficient information to discriminate porcine esophagus tissues.

Engineering approaches for characterizing soft tissue mechanical properties: A review

From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and functionality. Alterations in the structural features of tissues are often manifested as local stiffening and are early signs for diagnosing a disease. These elastic properties are measured ex vivo in conventional mechanical testing regimes, however, the heterogeneous microstructure of tissues can be accurately resolved over relatively smaller length scales with enhanced spatial resolution using techniques such as micro-indentation, microelectromechanical (MEMS) based cantilever sensors and optical catheters which also facilitate in vivo assessment of mechanical properties. In this review, we describe several probing strategies (qualitative and quantitative) based on the spatial scale of mechanical assessment and also discuss the potential use of machine learning techniques to compute the mechanical properties of soft tissues. This work details state of the art advancement in probing strategies, associated challenges toward quantitative characterization of tissue biomechanics both from an engineering and clinical standpoint.

Semi-automated 3D segmentation of human skeletal muscle using Focused Ion Beam-Scanning Electron Microscopic images

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) is an imaging approach that enables analysis of the 3D architecture of cells and tissues at resolutions that are 1-2 orders of magnitude higher than that possible with light microscopy. The slow speeds of data collection and manual segmentation are two critical problems that limit the more extensive use of FIB-SEM technology. Here, we present an easily accessible robust method that enables rapid, large-scale acquisition of data from tissue specimens, combined with an approach for semi-automated data segmentation using the open-source machine learning Weka segmentation software, which dramatically increases the speed of image analysis. We demonstrate the feasibility of these methods through the 3D analysis of human muscle tissue by showing that our process results in an improvement in speed of up to three orders of magnitude as compared to manual approaches for data segmentation. All programs and scripts we use are open source and are immediately available for use by others.

Structured polarized light microscopy for collagen fiber structure and orientation quantification in thick ocular tissues

Collagen is a major constituent of the eye and understanding its architecture and biomechanics is critical to preserve and restore vision. We, recently, demonstrated polarized light microscopy (PLM) as a powerful technique for measuring properties of the collagen fibers of the eye, such as spatial distribution and orientation. Our implementation of PLM, however, required sectioning the tissues for imaging using transmitted light. This is problematic because it limits analysis to thin sections. This is not only slow, but precludes study of dynamic events such as pressure-induced deformations, which are central to the role of collagen. We introduce structured polarized light microscopy (SPLM), an imaging technique that combines structured light illumination with PLM to allow imaging and measurement of collagen fiber properties in thick ocular tissues. Using pig and sheep eyes, we show that SPLM rejects diffuse background light effectively in thick tissues, significantly enhancing visualization of optic nerve head (ONH) structures, such as the lamina cribrosa, and improving the accuracy of the collagen fiber orientation measurements. Further, we demonstrate the integration of SPLM with an inflation device to enable direct visualization, deformation tracking, and quantification of collagen fibers in ONHs while under controlled pressure.