Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.OCIS codes: (100.0100) Image processing, (100.2960) Image analysis, (110.4500) Optical coherence tomography, (170.4470) Ophthalmology     

In vivo imaging of retinal hemodynamics with OCT angiography and Doppler OCT

Retinal hemodynamics is important for early diagnosis and precise monitoring in retinal vascular diseases. We propose a novel method for measuring absolute retinal blood flow in vivo using the combined techniques of optical coherence tomography (OCT) angiography and Doppler OCT. Doppler values can be corrected by Doppler angles extracted from OCT angiography images. A three-dimensional (3D) segmentation algorithm based on dynamic programming was developed to extract the 3D boundaries of optic disc vessels, and Doppler angles were calculated from 3D vessel geometry. The accuracy of blood flow from the Doppler OCT was validated using a flow phantom. The feasibility of the method was tested on a subject in vivo. The pulsatile retinal blood flow and the parameters for retinal hemodynamics were successfully obtained.OCIS codes: (100.2960) Image analysis, (100.6890) Three-dimensional image processing, (170.4500) Optical coherence tomography, (170.4460) Ophthalmic optics and devices     

Advanced image processing for optical coherence tomographic angiography of macular diseases

This article provides an overview of advanced image processing for three dimensional (3D) optical coherence tomographic (OCT) angiography of macular diseases, including age-related macular degeneration (AMD) and diabetic retinopathy (DR). A fast automated retinal layers segmentation algorithm using directional graph search was introduced to separates 3D flow data into different layers in the presence of pathologies. Intelligent manual correction methods are also systematically addressed which can be done rapidly on a single frame and then automatically propagated to full 3D volume with accuracy better than 1 pixel. Methods to visualize and analyze the abnormalities including retinal and choroidal neovascularization, retinal ischemia, and macular edema were presented to facilitate the clinical use of OCT angiography.OCIS codes: (110.4500) Optical coherence tomography, (100.0100) Image processing, (100.2960) Image analysis, (170.4470) Ophthalmology     

Framework for quantitative three-dimensional choroidal vasculature analysis using optical coherence tomography

Choroidal vasculature plays an important role in the pathogenesis of retinal diseases, such as myopic maculopathy, age-related macular degeneration, diabetic retinopathy, central serous chorioretinopathy, and ocular inflammatory diseases. Current optical coherence tomography (OCT) technology provides three-dimensional visualization of the choroidal angioarchitecture; however, quantitative measures remain challenging. Here, we propose and validate a framework to segment and quantify the choroidal vasculature from a prototype swept-source OCT (PLEX Elite 9000, Carl Zeiss Meditec, USA) using a 3×3 mm scan protocol centered on the macula. Enface images referenced from the retinal pigment epithelium were reconstructed from the volumetric data. The boundaries of the choroidal volume were automatically identified by tracking the choroidal vessel feature structure over the depth, and a selective sliding window was applied for segmenting the vessels adaptively from attenuation-corrected enface images. We achieved a segmentation accuracy of 96% ± 1% as compared with manual annotation, and a dice coefficient of 0.83 ± 0.04 for repeatability. Using this framework on both control (0.00 D to −2.00 D) and highly myopic (−8.00 D to −11.00 D) eyes, we report a decrease in choroidal vessel volume (p<0.001) in eyes with high myopia.     

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch’s membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch’s membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra’s algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice’s Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.OCIS codes: (100.0100) Image processing, (110.4500) Optical coherence tomography, (100.2960) Image analysis, (170.4470) Ophthalmology     

Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model

A two stage statistical model based on texture and shape for fully automatic choroidal segmentation of normal and pathologic eyes obtained by a 1060 nm optical coherence tomography (OCT) system is developed. A novel dynamic programming approach is implemented to determine location of the retinal pigment epithelium/ Bruch's membrane /choriocapillaris (RBC) boundary. The choroid-sclera interface (CSI) is segmented using a statistical model. The algorithm is robust even in presence of speckle noise, low signal (thick choroid), retinal pigment epithelium (RPE) detachments and atrophy, drusen, shadowing and other artifacts. Evaluation against a set of 871 manually segmented cross-sectional scans from 12 eyes achieves an average error rate of 13%, computed per tomogram as a ratio of incorrectly classified pixels and the total layer surface. For the first time a fully automatic choroidal segmentation algorithm is successfully applied to a wide range of clinical volumetric OCT data.     

Automated quantification of lung structures from optical coherence tomography images

Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis.OCIS codes: (110.4500) Optical coherence tomography, (100.0100) Image processing, (100.2960) Image analysis     

Volume-based,layer-independent,disease-agnostic detection of abnormal retinal reflectivity,nonperfusion, and neovascularization using structural and angiographic OCT

Optical coherence tomography (OCT) is widely used in ophthalmic practice because it can visualize retinal structure and vasculature in vivo and 3-dimensionally (3D). Even though OCT procedures yield data volumes, clinicians typically interpret the 3D images using two-dimensional (2D) data subsets, such as cross-sectional scans or en face projections. Since a single OCT volume can contain hundreds of cross-sections (each of which must be processed with retinal layer segmentation to produce en face images), a thorough manual analysis of the complete OCT volume can be prohibitively time-consuming. Furthermore, 2D reductions of the full OCT volume may obscure relationships between disease progression and the (volumetric) location of pathology within the retina and can be prone to mis-segmentation artifacts. In this work, we propose a novel framework that can detect several retinal pathologies in three dimensions using structural and angiographic OCT. Our framework operates by detecting deviations in reflectance, angiography, and simulated perfusion from a percent depth normalized standard retina created by merging and averaging scans from healthy subjects. We show that these deviations from the standard retina can highlight multiple key features, while the depth normalization obviates the need to segment several retinal layers. We also construct a composite pathology index that measures average deviation from the standard retina in several categories (hypo- and hyper-reflectance, nonperfusion, presence of choroidal neovascularization, and thickness change) and show that this index correlates with DR severity. Requiring minimal retinal layer segmentation and being fully automated, this 3D framework has a strong potential to be integrated into commercial OCT systems and to benefit ophthalmology research and clinical care.     

Non-invasive measurement of choroidal volume change and ocular rigidity through automated segmentation of high-speed OCT imaging

We have developed a novel optical approach to determine pulsatile ocular volume changes using automated segmentation of the choroid, which, together with Dynamic Contour Tonometry (DCT) measurements of intraocular pressure (IOP), allows estimation of the ocular rigidity (OR) coefficient. Spectral Domain Optical Coherence Tomography (OCT) videos were acquired with Enhanced Depth Imaging (EDI) at 7Hz during ~50 seconds at the fundus. A novel segmentation algorithm based on graph search with an edge-probability weighting scheme was developed to measure choroidal thickness (CT) at each frame. Global ocular volume fluctuations were derived from frame-to-frame CT variations using an approximate eye model. Immediately after imaging, IOP and ocular pulse amplitude (OPA) were measured using DCT. OR was calculated from these peak pressure and volume changes. Our automated segmentation algorithm provides the first non-invasive method for determining ocular volume change due to pulsatile choroidal filling, and the estimation of the OR constant. Future applications of this method offer an important avenue to understanding the biomechanical basis of ocular pathophysiology.OCIS codes: (170.3880) Medical and biological imaging, (170.4460) Ophthalmic optics and devices, (170.6935) Tissue characterization     

Impact of intraocular pressure on changes of blood flow in the retina, choroid, and optic nerve head in rats investigated by optical microangiography

In this paper, we demonstrate the use of optical coherence tomography/optical microangiography (OCT/OMAG) to image and measure the effects of acute intraocular pressure (IOP) elevation on retinal, choroidal and optic nerve head (ONH) perfusion in the rat eye. In the experiments, IOP was elevated from 10 to 100 mmHg in 10 mmHg increments. At each IOP level, three-dimensional data volumes were captured using an ultrahigh sensitive (UHS) OMAG scanning protocol for 3D volumetric perfusion imaging, followed by repeated B-scans for Doppler OMAG analysis to determine blood flow velocity. Velocity and vessel diameter measurements were used to calculate blood flow in selected retinal blood vessels. Choroidal perfusion was calculated by determining the peripapillary choroidal filling at each pressure level and calculating this as a percentage of area filling at baseline (10 mmHg). ONH blood perfusion was calculated as the percentage of blood flow area over a segmented ONH area to a depth 150 microns posterior to the choroidal opening. We show that volumetric blood flow reconstructions revealed detailed 3D maps, to the capillary level, of the retinal, choroidal and ONH microvasculature, revealing retinal arterioles, capillaries and veins, the choroidal opening and a consistent presence of the central retinal artery inferior to the ONH. While OCT structural images revealed a reversible compression of the ONH and vasculature with elevated IOP, OMAG successfully documented changes in retinal, choroidal and ONH blood perfusion and allowed quantitative measurements of these changes. Starting from 30 mm Hg, retinal blood flow (RBF) diminished linearly with increasing IOP and was nearly extinguished at 100 mm Hg, with full recovery after return of IOP to baseline. Choroidal filling was unaffected until IOP reached 60 mmHg, then decreased to 20% of baseline at IOP 100 mmHg, and normalized when IOP returned to baseline. A reduction in ONH blood perfusion at higher IOP’s was also observed, but shadow from overlying retinal vessels at lower IOP’s limited precise measurements of changes in ONH capillary perfusion compared to baseline. Therefore, OCT/OMAG can be a useful tool to image and measure blood flow in the retina, choroidal and ONH of the rat eye as well as document the effects of elevated IOP on blood flow in these vascular beds.OCIS codes: (170.4460) Ophthalmic optics and devices, (170.3880) Medical and biological imaging, (170.4500) Optical coherence tomography     

Improved measurement of vibration amplitude in dynamic optical coherence elastography

Abstract: Optical coherence elastography employs optical coherence tomography (OCT) to measure the displacement of tissues under load and, thus, maps the resulting strain into an image, known as an elastogram. We present a new improved method to measure vibration amplitude in dynamic optical coherence elastography. The tissue vibration amplitude caused by sinusoidal loading is measured from the spread of the Doppler spectrum, which is extracted using joint spectral and time domain signal processing. At low OCT signal-to-noise ratio (SNR), the method provides more accurate vibration amplitude measurements than the currently used phase-sensitive method. For measurements performed on a mirror at OCT SNR = 5 dB, our method introduces <3% error, compared to >20% using the phase-sensitive method. We present elastograms of a tissue-mimicking phantom and excised porcine tissue that demonstrate improvements, including a 50% increase in the depth range of reliable vibration amplitude measurement.OCIS codes: (110.4500) Optical coherence tomography, (290.5820) Scattering measurements, (170.6935) Tissue characterization     

In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading

Optical coherence tomography (OCT) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. This technique is particularly useful for studying coronary atherosclerosis. In this paper, we present a new framework that allows a segmentation and quantification of OCT images of coronary arteries to define the plaque type and stenosis grading. These analyses are usually carried out on-line on the OCT-workstation where measuring is mainly operator-dependent and mouse-based. The aim of this program is to simplify and improve the processing of OCT images for morphometric investigations and to present a fast procedure to obtain 3D geometrical models that can also be used for external purposes such as for finite element simulations. The main phases of our toolbox are the lumen segmentation and the identification of the main tissues in the artery wall. We validated the proposed method with identification and segmentation manually performed by expert OCT readers. The method was evaluated on ten datasets from clinical routine and the validation was performed on 210 images randomly extracted from the pullbacks. Our results show that automated segmentation of the vessel and of the tissue components are possible off-line with a precision that is comparable to manual segmentation for the tissue component and to the proprietary-OCT-console for the lumen segmentation. Several OCT sections have been processed to provide clinical outcome.     

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.     

Quantitative OCT angiography of optic nerve head blood flow

Optic nerve head (ONH) blood flow may be associated with glaucoma development. A reliable method to quantify ONH blood flow could provide insight into the vascular component of glaucoma pathophysiology. Using ultrahigh-speed optical coherence tomography (OCT), we developed a new 3D angiography algorithm called split-spectrum amplitude-decorrelation angiography (SSADA) for imaging ONH microcirculation. In this study, a method to quantify SSADA results was developed and used to detect ONH perfusion changes in early glaucoma. En face maximum projection was used to obtain 2D disc angiograms, from which the average decorrelation values (flow index) and the percentage area occupied by vessels (vessel density) were computed from the optic disc and a selected region within it. Preperimetric glaucoma patients had significant reductions of ONH perfusion compared to normals. This pilot study indicates OCT angiography can detect the abnormalities of ONH perfusion and has the potential to reveal the ONH blood flow mechanism related to glaucoma.OCIS codes: (170.4500) Optical coherence tomography, (170.3880) Medical and biological imaging, (170.4470) Ophthalmology     

Optical microangiography of retina and choroid and measurement of total retinal blood flow in mice

We present a novel application of optical microangiography (OMAG) imaging technique for visualization of depth-resolved vascular network within retina and choroid as well as measurement of total retinal blood flow in mice. A fast speed spectral domain OCT imaging system at 820nm with a line scan rate of 140 kHz was developed to image the posterior segment of eyes in mice. By applying an OMAG algorithm to extract the moving blood flow signals out of the background tissue, we are able to provide true capillary level imaging of the retinal and choroidal vasculature. The microvascular patterns within different retinal layers are presented. An en face Doppler OCT approach [Srinivasan et al., Opt Express 18, 2477 (2010)] was adopted for retinal blood flow measurement. The flow is calculated by integrating the axial blood flow velocity over the vessel area measured in an en face plane without knowing the blood vessel angle. Total retinal blood flow can be measured from both retinal arteries and veins. The results indicate that OMAG has the potential for qualitative and quantitative evaluation of the microcirculation in posterior eye compartments in mouse models of retinopathy and neovascularization.OCIS codes: (170.4500) Optical coherence tomography, (170.3880) Medical and biological imaging     

Retinal, anterior segment and full eye imaging using ultrahigh speed swept source OCT with vertical-cavity surface emitting lasers

We demonstrate swept source OCT utilizing vertical-cavity surface emitting laser (VCSEL) technology for in vivo high speed retinal, anterior segment and full eye imaging. The MEMS tunable VCSEL enables long coherence length, adjustable spectral sweep range and adjustable high sweeping rate (50–580 kHz axial scan rate). These features enable integration of multiple ophthalmic applications into one instrument. The operating modes of the device include: ultrahigh speed, high resolution retinal imaging (up to 580 kHz); high speed, long depth range anterior segment imaging (100 kHz) and ultralong range full eye imaging (50 kHz). High speed imaging enables wide-field retinal scanning, while increased light penetration at 1060 nm enables visualization of choroidal vasculature. Comprehensive volumetric data sets of the anterior segment from the cornea to posterior crystalline lens surface are also shown. The adjustable VCSEL sweep range and rate make it possible to achieve an extremely long imaging depth range of ~50 mm, and to demonstrate the first in vivo 3D OCT imaging spanning the entire eye for non-contact measurement of intraocular distances including axial eye length. Swept source OCT with VCSEL technology may be attractive for next generation integrated ophthalmic OCT instruments.OCIS codes: (110.4500) Optical coherence tomography, (120.4640) Optical instruments, (140.3600) Lasers, tunable, (170.4460) Ophthalmic optics and devices, (170.4470) Ophthalmology     

Deep learning toolbox for automated enhancement,segmentation, and graphing of cortical optical coherence tomography microangiograms

Optical coherence tomography angiography (OCTA) is becoming increasingly popular for neuroscientific study, but it remains challenging to objectively quantify angioarchitectural properties from 3D OCTA images. This is mainly due to projection artifacts or “tails” underneath vessels caused by multiple-scattering, as well as the relatively low signal-to-noise ratio compared to fluorescence-based imaging modalities. Here, we propose a set of deep learning approaches based on convolutional neural networks (CNNs) to automated enhancement, segmentation and gap-correction of OCTA images, especially of those obtained from the rodent cortex. Additionally, we present a strategy for skeletonizing the segmented OCTA and extracting the underlying vascular graph, which enables the quantitative assessment of various angioarchitectural properties, including individual vessel lengths and tortuosity. These tools, including the trained CNNs, are made publicly available as a user-friendly toolbox for researchers to input their OCTA images and subsequently receive the underlying vascular network graph with the associated angioarchitectural properties.     

Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer: accuracy of the ZBS algorithm

We evaluate the accuracy of a vascular segmentation algorithm which uses continuity in the maximum intensity projection (MIP) depth Z-buffer as a pre-processing step to generate a list of 3D seed points for further segmentation. We refer to the algorithm as Z-buffer segmentation (ZBS). The pre-processing of the MIP Z-buffer is based on smoothness measured using the minimum chi-square value of a least square fit. Points in the Z-buffer with chi-square values below a selected threshold are used as seed points for 3D region growing. The ZBS algorithm couples spatial continuity information with intensity information to create a simple yet accurate segmentation algorithm. We examine the dependence of the segmentation on various parameters of the algorithm. Performance is assessed in terms of the inclusion/exclusion of vessel/background voxels in the segmentation of intracranial time-of-flight MRA images. The evaluation is based on 490,256 voxels from 14 patients which were classified by an observer. ZBS performance was compared to simple thresholding and to segmentation based on vessel enhancement filtering. The ZBS segmentation was only weakly dependent on the parameters of the initial MIP image generation, indicating the robustness of this approach. Region growing based on Z-buffer generated seeds was advantageous compared to simple thresholding. The ZBS algorithm provided segmentation accuracies similar to that obtained with the vessel enhancement filter. The ZBS performance was notably better than the filter based segmentation for aneurysms where the assumptions of the filter were violated. As currently implemented the algorithm slightly under-segments the intracranial vasculature.     

In vivo detection of cortical optical changes associated with seizure activity with optical coherence tomography

The most common technology for seizure detection is with electroencephalography (EEG), which has low spatial resolution and minimal depth discrimination. Optical techniques using near-infrared (NIR) light have been used to improve upon EEG technology and previous research has suggested that optical changes, specifically changes in near-infrared optical scattering, may precede EEG seizure onset in in vivo models. Optical coherence tomography (OCT) is a high resolution, minimally invasive imaging technique, which can produce depth resolved cross-sectional images. In this study, OCT was used to detect changes in optical properties of cortical tissue in vivo in mice before and during the induction of generalized seizure activity. We demonstrated that a significant decrease (P < 0.001) in backscattered intensity during seizure progression can be detected before the onset of observable manifestations of generalized (stage-5) seizures. These results indicate the feasibility of minimally-invasive optical detection of seizures with OCT.OCIS codes: (110.4500) Optical coherence tomography, (170.3880) Medical and biological imaging, (100.2960) Image analysis