Potential measurement error from vessel reflex and multiple light paths in dual-wavelength retinal oximetry

PURPOSE

This study aims to characterize the dependence of measured retinal arterial and venous saturation on vessel diameter and central reflex in retinal oximetry, with an ultimate goal of identifying potential causes and suggesting approaches to improve measurement accuracy.

METHODS

In 10 subjects, oxygen saturation, vessel diameter and optical density are obtained using Oxymap Analyzer software without diameter correction. Diameter dependence of saturation is characterized using linear regression between measured values of saturation and diameter. Occurrences of negative values of vessel optical densities (ODs) associated with central vessel reflex are acquired from Oxymap Analyzer. A conceptual model is used to calculate the ratio of optical densities (ODRs) according to retinal reflectance properties and single and double-pass light transmission across fixed path lengths. Model-predicted values are compared with measured oximetry values at different vessel diameters.

RESULTS

Venous saturation shows an inverse relationship with vessel diameter (D) across subjects, with a mean slope of -0.180 (SE = 0.022) %/μm (20 < D < 180 μm) and a more rapid saturation increase at small vessel diameters reaching to over 80%. Arterial saturation yields smaller positive and negative slopes in individual subjects, with an average of -0.007 (SE = 0.021) %/μm (20 < D < 200 μm) across all subjects. Measurements where vessel brightness exceeds that of the retinal background result in negative values of optical density, causing an artifactual increase in saturation. Optimization of model reflectance values produces a good fit of the conceptual model to measured ODRs.

CONCLUSION

Measurement artefacts in retinal oximetry are caused by strong central vessel reflections, and apparent diameter sensitivity may result from single and double-pass transmission in vessels. Improvement in correction for vessel diameter is indicated for arteries however further study is necessary for venous corrections.

Study protocol - Prospective case-control trial - Impact of significant carotid stenosis on retinal perfusion measured with automated retinal oximetry

BACKGROUND

Large vessel carotid stenosis is a significant cause of ischaemic stroke. Indications for surgical revascularisation depend on the severity of the stenosis and clinical symptoms. However, mild symptoms such as TIA (Transient ischaemic attack), amaurosis fugax or minor stroke precede large strokes in only 15% of cases.

AIM

The aim of this prospective study is to evaluate whether retinal perfusion is impacted in significant carotid stenosis. Automated retinal oximetry will be used to better assess perfusion in the post-stenotic basin. We presume the more stenotic the blood vessel, the more reduced the retinal perfusion is, resulting in adaptive changes such as greater arteriovenous saturation difference due to greater oxygen extraction. This could broaden the indication spectrum for revascularisation for carotid stenosis.

METHODS

We plan to enroll yearly 50 patients with significant carotid stenosis and cross-examine them with retinal oximetry. The study group will provide stenotic vessels and, non-stenotic vessels will form the control group. Patients with significant carotid stenosis will undergo an MRI (Magnetic Resonnance imaging) examination to determine the presence of asymptomatic recent ischaemic lesions in the stenotic basin, and the correlation to oximetry parameters.

STATISTICS

The stenosis severity and retinal oximetry parameters will be compared for study and control groups with a threshold of 70%, respectively 80% and 90% stenosis. Results will be then reevaluated with emphasis on MRI findings in the carotid basin.

CONCLUSION

This prospective case control study protocol will be used to launch a multicentre trial assessing the relationship between significant carotid stenosis and retinal perfusion measured with automated retinal oximetry. Despite these differences, the findings indicate the potential of retinal oximetry for noninvasive real-time measurements of oxyhaemoglobin saturation in central nervous system vessels. Following calibration upgrade and technological improvement, verification retinal oximetry may potentially be applied to critically ill and anaesthesia care patients. The study on combined scanning laser ophthalmoscope and retinal oximetry supports the feasibility of the technique for oximetry analysis in newly born babies.

TRIAL REGISTRATION

ClinicalTrials.gov, ID: NCT06085612.

Advances in Retinal Oximetry

Similar to other organs, the retina relies on tightly regulated perfusion and oxygenation. Previous studies have demonstrated that retinal blood flow is affected in a variety of eye and systemic diseases, including diabetic retinopathy, age-related macular degeneration, and glaucoma. Although measurement of peripheral oxygen saturation has become a standard clinical measurement through the development of pulse oximetry, developing a noninvasive technique to measure retinal oxygen saturation has proven challenging, and retinal oximetry technology currently remains inadequate for reliable clinical use. Here, we review current strategies and approaches, as well as several newer technologies in development, and discuss the future of retinal oximetry.

Non-mydriatic chorioretinal imaging in a transmission geometry and application to retinal oximetry

The human retina is typically imaged in a reflection geometry, where light is delivered through the pupil and images are formed from the light reflected back from the retina. In this configuration, artifacts caused by retinal surface reflex are often encountered, which complicate quantitative interpretation of the reflection images. We present an alternative illumination method, which avoids these artifacts. The method uses deeply penetrating near-infrared (NIR) light delivered transcranially from the side of the head, and exploits multiple scattering to redirect a portion of the light towards the posterior eye. This unique transmission geometry simplifies absorption measurements and enables flash-free, non-mydriatic imaging as deep as the choroid. Images taken with this new transillumination approach are applied to retinal oximetry.

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach

The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.

Automatic labeling of portal and hepatic veins from MR images prior to liver transplantation

PURPOSE

Living donated liver transplantation is an important task since a person (healthy donor) donates some part of her/his liver to a person in this surgery operation. The success of this operation mainly depends on the sufficiency of vessels and volume of the liver. Accurate labeling of portal and hepatic veins of donors reduces the incidence of complications during and after transplantation. Therefore, prior to the hepatic surgery, automatic analysis and labeling of vasculature structures in the liver are vital to see whether liver is suitable or not for transplantation. However, automatic labeling of veins in the liver is challenging because of partial volume effects, noise and image resolution, which causes wrong connections between vessels. The goal of this paper is to propose an automatic labeling approach for vessels.

METHODS

The proposed automated labeling method is based on gray-level values in the MR images and anatomical information. In this work, detection and segmentation of vascular structures in the liver is performed automatically with clustering-based segmentation and refinement stages.

RESULTS

The accuracy of the automatic labeling approach is 85 %. Required processing time for the proposed method (average 6 s) is shorter than manual approach (average 295 s) for labeling of hepatic and portal veins from segmented vessels.

CONCLUSION

The proposed approach is efficient in terms of both computational cost and accuracy of labeling and segmentation of hepatic and portal veins.

Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.

Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

Blood vessel segmentation methodologies in retinal images--a survey

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

An automated computational framework for retinal vascular network labeling and branching order analysis

Changes in retinal vascular morphology are well known as predictive clinical signs of many diseases such as hypertension, diabetes and so on. Computer-aid image processing and analysis for retinal vessels in fundus images are effective and efficient in clinical diagnosis instead of tedious manual labeling and measurement. An automated computational framework for retinal vascular network labeling and analysis is presented in this work. The framework includes 1) detecting and locating the optic disc; 2) tracking the vessel centerline from detected seed points and linking the breaks after tracing; 3) extracting all the retinal vascular trees and identifying all the significant points; and 4) classifying terminal points into starting points and ending points based on the information of optic disc location, and finally assigning branch order for each extracted vascular tree in the image. All the modules in the framework are fully automated. Based on the results, morphological analysis is then applied to achieve geometrical and topological features based on branching order for one individual vascular tree or for the vascular network through the retinal vascular network in the images. Validation and experiments on the public DRIVE database have demonstrated that the proposed framework is a novel approach to analyze and study the vascular network pattern, and may offer new insights to the diagnosis of retinopathy.

Fast retinal vessel detection and measurement using wavelets and edge location refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.

Scale-invariant vesselness filtering for simultaneous extraction of optimal medial and boundary paths in retinal images

In this paper we present a new multiscale vesselness filtering technique to simultaneously compute optimal medial axes and boundaries on fundus images of the retina. The scale-invariance of the vessel cross-sectional profile in the frequency domain is examined, and phase congruency implementing the scale-invariance is utilised for multiscale vesselness filtering. This allows the vessel ridge and boundary evidence to be preserved under single filtering settings. We have performed width measurement experiments on a dataset of fundus images, using an optimal medial axis skeletonisation scheme as a post-processing step, to compare the proposed technique with a generalised Gaussian profile modelling approach.

Automatic blood vessel localization in small field of view eye images

Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also vessel widths, which vary widely in these images. We present an automatic technique for localizing vessels in small field of view images using multi-scale matched filters. We also estimate local vessel properties - width and orientation - along the length of each vessel. Furthermore, we explicitly account for highlights on thick vessels - central reflexes - which are ignored in many previous works. Qualitative and quantitative results demonstrate the efficacy of our method - e.g. vessel centers are localized with RMS and median errors of 2.11 and 1 pixels, respectively in 700×700 images.