Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.
Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to fund increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures.
Optical coherence tomography (OCT) of the human retina is now becoming established as an important modality for the detection and tracking of various ocular diseases. Voxel based morphometry (VBM) is a long standing neuroimaging analysis technique that allows for the exploration of the regional differences in the brain. There has been limited work done in developing registration based methods for OCT, which has hampered the advancement of VBM analyses in OCT based population studies. Following on from our recent development of an OCT registration method, we explore the potential benefits of VBM analysis in cohorts of healthy controls (HCs) and multiple sclerosis (MS) patients. Specifically, we validate the stability of VBM analysis in two pools of HCs showing no significant difference between the two populations. Additionally, we also present a retrospective study of age and sex matched HCs and relapsing remitting MS patients, demonstrating results consistent with the reported literature while providing insight into the retinal changes associated with this MS subtype.
Optical coherence tomography (OCT) is a noninvasive imaging modality that has begun to find widespread use in retinal imaging for the detection of a variety of ocular diseases. In addition to structural changes in the form of altered retinal layer thicknesses, pathological conditions may also cause the formation of edema within the retina. In multiple sclerosis, for instance, the nerve fiber and ganglion cell layers are known to thin. Additionally, the formation of pseudocysts called microcystic macular edema (MME) have also been observed in the eyes of about 5% of MS patients, and its presence has been shown to be correlated with disease severity. Previously, we proposed separate algorithms for the segmentation of retinal layers and MME, but since MME mainly occurs within specific regions of the retina, a simultaneous approach is advantageous. In this work, we propose an automated globally optimal graph-theoretic approach that simultaneously segments the retinal layers and the MME in volumetric OCT scans. SD-OCT scans from one eye of 12 MS patients with known MME and 8 healthy controls were acquired and the pseudocysts manually traced. The overall precision and recall of the pseudocyst detection was found to be 86.0% and 79.5%, respectively.
Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging modality that allows for the quantitative assessment of corneal thicknesses. Automated approaches for these measurements are not readily available and therefore measurements are often obtained manually. While graph-based approaches that enable the optimal simultaneous segmentation of multiple 3D surfaces have been proposed and applied to 3D optical coherence tomography volumes of the back of the eye, such approaches have not been applied for the segmentation of the corneal surfaces. In this work we propose adapting this graph-based method for the automated 3D segmentation of three corneal surfaces in AS-OCT images and to measure total corneal thickness. The approach is evaluated based on 34 AS-OCT volumes obtained from both eyes of 17 mice with varying corneal thicknesses. The segmentation accuracy was assessed using unsigned border positioning errors and was found to be 1.82 +/- 0.81 μm. We also assessed an average relative error in total layer thickness measurements which was found to be 2.27%.
Spectral-domain optical coherence tomography (SD-OCT) finds widespread use clinically for the detection and management of ocular diseases. This non-invasive imaging modality has also begun to find frequent use in research studies involving animals such as mice. Numerous approaches have been proposed for the segmentation of retinal surfaces in SD-OCT images obtained from human subjects; however, the segmentation of retinal surfaces in mice scans is not as well-studied. In this work, we describe a graph-theoretic segmentation approach for the simultaneous segmentation of 10 retinal surfaces in SD-OCT scans of mice that incorporates learned shape priors. We compared the method to a baseline approach that did not incorporate learned shape priors and observed that the overall unsigned border position errors reduced from 3.58 +/- 1.33 μm to 3.20 +/- 0.56 μm.
While efficient graph-theoretic approaches exist for the optimal (with respect to a cost function) and simultaneous
segmentation of multiple surfaces within volumetric medical images, the appropriate design of cost functions
remains an important challenge. Previously proposed methods have used simple cost functions or optimized a
combination of the same, but little has been done to design cost functions using learned features from a training
set, in a less biased fashion. Here, we present a method to design cost functions for the simultaneous segmentation
of multiple surfaces using the graph-theoretic approach. Classified texture features were used to create probability
maps, which were incorporated into the graph-search approach. The efficiency of such an approach was tested
on 10 optic nerve head centered optical coherence tomography (OCT) volumes obtained from 10 subjects that
presented with glaucoma. The mean unsigned border position error was computed with respect to the average of
manual tracings from two independent observers and compared to our previously reported results. A significant
improvement was noted in the overall means which reduced from 9.25 ± 4.03μm to 6.73 ± 2.45μm (p < 0.01)
and is also comparable with the inter-observer variability of 8.85 ± 3.85μm.
Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in
the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL)
has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with
spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an
automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D
spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected
simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed
using edge/regional information, and subject to a priori determined varying surface interaction and smoothness
constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic
disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric
scans, and the border positioning error was found to be 7.25 ± 1.08 μm and 8.94 ± 3.76 μm for the normal and
glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous
scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 ± 32.7 μm in
normal eyes versus 60.4 ± 25.2 μm in glaucomatous eyes).
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