SVM-based texture classification in optical coherence tomography

This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1

Extracting structural features of rat sciatic nerve using polarization-sensitive spectral domain optical coherence tomography

We present spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) imaging of peripheral nerves. Structural and polarization-sensitive OCT imaging of uninjured rat sciatic nerves was evaluated both qualitatively and quantitatively. OCT and its functional extension, PS-OCT, were used to image sciatic nerve structure with clear delineation of the nerve boundaries to muscle and adipose tissues. A long-known optical effect, bands of Fontana, was also observed. Postprocessing analysis of these images provided significant quantitative information, such as epineurium thickness, estimates of extinction coefficient and birefringence of nerve and muscle tissue, frequency of bands of Fontana at different stretch levels of nerve, and change in average birefringence of nerve under stretched condition. We demonstrate that PS-OCT combined with regular-intensity OCT (compared with OCT alone) allows for a clearer determination of the inner and outer boundaries of the epineurium and distinction of nerve and muscle based on their birefringence pattern. PS-OCT measurements on normal nerves show that the technique is promising for studies on peripheral nerve injury. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)

Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis

GPU accelerated real-time multi-functional spectral-domain optical coherence tomography system at 1300 nm.

We present a GPU accelerated multi-functional spectral domain optical coherence tomography system at 1300 nm. The system is capable of real-time processing and display of every intensity image, comprised of 512 pixels by 2048 A-lines acquired at 20 frames per second. The update rate for all four images with size of 512 pixels by 2048 A-lines simultaneously (intensity, phase retardation, flow and en face view) is approximately 10 frames per second. Additionally, we report for the first time the characterization of phase retardation and diattenuation by a sample comprised of a stacked set of polarizing film and wave plate. The calculated optic axis orientation, phase retardation and diattenuation match well with expected values. The speed of each facet of the multi-functional OCT CPU-GPU hybrid acquisition system, intensity, phase retardation, and flow, were separately demonstrated by imaging a horseshoe crab lateral compound eye, a non-uniformly heated chicken muscle, and a microfluidic device. A mouse brain with thin skull preparation was imaged in vivo and demonstrated the capability of the system for live multi-functional OCT visualization

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease

AbstractThree-dimensional tissue-structural relationships are not well captured by typical thin-section histology, posing challenges for the study of tissue physiology and pathology. Moreover, while recent progress has been made with intact methods for clearing, labeling, and imaging whole organs such as the mature brain, these approaches are generally unsuitable for soft, irregular, and heterogeneous tissues that account for the vast majority of clinical samples and biopsies. Here we develop a biphasic hydrogel methodology, which along with automated analysis, provides for high-throughput quantitative volumetric interrogation of spatially-irregular and friable tissue structures. We validate and apply this approach in the examination of a variety of developing and diseased tissues, with specific focus on the dynamics of normal and pathological pancreatic innervation and development, including in clinical samples. Quantitative advantages of the intact-tissue approach were demonstrated compared to conventional thin-section histology, pointing to broad applications in both research and clinical settings.</jats:p

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific