Laser speckle flowgraphy (LSFG) is a non-invasive imaging technology for quantifying microvascular blood flow. In the eye, LSFG quantifies the relative dynamics of blood flow of the retina, choroid and optic nerve head on a continuous scale. Currently, LSFG analysis requires the placement of “rubber bands” (defining regions of interest) to measure blood flow at desired locations. However, the placement of rubber bands requires knowledge of which regions are likely to be affected by disease. Here, we demonstrate a fully automated superpixel-histogram method without rubber band placement to determine regional blood flow abnormalities. Regional blood flow patterns were quantified via superpixel distributions of mean blur rate (MBR, linearly proportional to blood flow) and percentages of total superpixels at five pre-defined ranges of blood flow. We applied the proposed method to help diagnose acute arteritic anterior ischemic optic neuropathy (AAION) and found that compared to normal eyes, acute AAION eyes showed a significant blood flow reduction of the choroid due to the effect of giant cell arteritis on the posterior ciliary arteries (supplying the choroid and optic nerve circulation). The proposed method demonstrated a novel approach for quantifying abnormal regions of blood flow in different vascular beds caused by disease.
Glaucoma is one of the leading causes of permanent blindness due to optic nerve damage. Optical coherence tomography (OCT) has become an important clinical tool for assessing structural damage from the loss of neurons. Traditional 2D and 3D methods have been successfully applied to quantify inner retinal layer thickness. However, these methods show less reliable segmentation in severe glaucoma when the retinal layers have become thin and violate algorithm assumptions. Deep learning (DL) is an alternative image analysis approach due to its powerful ability to extract features directly from data. State-of-the-art DL segmentation approaches can achieve sub-pixel accuracy at multiple retinal surfaces in OCT scans from normal eyes. However, limitations, such as spike-like segmentation errors (showing as high Hausdorff distances) and lack of contextual information from the input image, still need to be improved. To address these limitations, three novel solutions were proposed in this study. First, for data augmentation, we reconstructed more B-scans by reassembling A-scans at the vertical and jittered planes to expose DL to a greater variety of features encountered in OCT. Second, smoothed and contrast-enhanced images of each three adjacent B-scans were concatenated to provide a six-channel input image stack to the neural network with contextual information. Finally, we merged the predicted surfaces from both horizontal and vertical B-scans while maintaining retinal topological order. In our independently tested dataset, which included eyes with severe glaucoma, the proposed approach outperformed the state-of-the-art methods in mean absolute surface distances, Dice coefficients, and Hausdorff distance at multiple surfaces.
In cases of optic-nerve-head edema, the presence of the swelling reduces the visibility of the underlying neural canal opening (NCO) within spectral-domain optical coherence tomography (SD-OCT) volumes. Consequently, traditional SD-OCT-based NCO segmentation methods often overestimate the size of the NCO. The visibility of the NCO can be improved using high-definition 2D raster scans, but such scans do not provide 3D contextual image information. In this work, we present a semi-automated approach for the segmentation of the NCO in cases of optic disc edema by combining image information from volumetric and high-definition raster SD-OCT image sequences. In particular, for each subject, five high-definition OCT B-scans and the OCT volume are first separately segmented, and then the five high-definition B-scans are automatically registered to the OCT volume. Next, six NCO points are placed (manually, in this work) in the central three high-definition OCT B-scans (two points for each central B-scans) and are automatically transferred into the OCT volume. Utilizing a combination of these mapped points and the 3D image information from the volumetric scans, a graph-based approach is used to identify the complete NCO on the OCT en-face image. The segmented NCO points using the new approach were significantly closer to expert-marked points than the segmented NCO points using a traditional approach (root mean square differences in pixels: 5.34 vs. 21.71, p < 0.001).
Swelling of the optic nerve head (ONH) is subjectively assessed by clinicians using the Frisén scale. It is believed that a direct measurement of the ONH volume would serve as a better representation of the swelling. However, a direct measurement requires optic nerve imaging with spectral domain optical coherence tomography (SD-OCT) and 3D segmentation of the resulting images, which is not always available during clinical evaluation. Furthermore, telemedical imaging of the eye at remote locations is more feasible with non-mydriatic fundus cameras which are less costly than OCT imagers. Therefore, there is a critical need to develop a more quantitative analysis of optic nerve swelling on a continuous scale, similar to SD-OCT. Here, we select features from more commonly available 2D fundus images and use them to predict ONH volume. Twenty-six features were extracted from each of 48 color fundus images. The features include attributes of the blood vessels, optic nerve head, and peripapillary retina areas. These features were used in a regression analysis to predict ONH volume, as computed by a segmentation of the SD-OCT image. The results of the regression analysis yielded a mean square error of 2.43 mm3 and a correlation coefficient between computed and predicted volumes of R = 0:771, which suggests that ONH volume may be predicted from fundus features alone.
Recent studies have shown that the Bruch's membrane (BM) and retinal pigment epithelium (RPE), visualized on spectral-domain optical coherence tomography (SD-OCT), is deformed anteriorly towards the vitreous in patients with intracranial hypertension and papilledema. The BM/RPE shape has been quantified using a statistical-shape-model approach; however, to date, the approach has involved the tedious and time-consuming manual placement of landmarks and correspondingly, only the shape (and shape changes) of a limited number of patients has been studied. In this work, we first present a semi-automated approach for the extraction of 20 landmarks along the BM from an optic-nerve-head (ONH) centered OCT slice from each patient. In the approach, after the manual placement of the two Bruch's membrane opening (BMO) points, the remaining 18 landmarks are automatically determined using a graph-based segmentation approach. We apply the approach to the OCT scans of 116 patients (at baseline) enrolled in the Idiopathic Intracranial Hypertension Treatment Trial and generate a statistical shape model using principal components analysis. Using the resulting shape model, the coefficient (shape measure) corresponding to the second principal component (eigenvector) for each set of landmarks indicates the degree of the BM/RPE is oriented away from the vitreous. Using a subset of 20 patients, we compare the shape measure computed using this semi-automated approach with the resulting shape measure when (1) all landmarks are specified manually (Experiment I); and (2) a different expert specifies the two BMO points (Experiment II). In each case, a correlation coefficient ≥ 0.99 is obtained.
The six-stage Frisén scale is a qualitative and subjective method for assessing papilledema (optic disc swelling due to raised intracranial pressure) using fundus photographs. The recent introduction of spectral-domain optical coherence tomography (SD-OCT) presents a promising alternative to enable the 3-D quantitative estimation of papilledema. In this work, we propose an automated region-based volumetric estimation of the degree of papilledema from SD-OCT. After using a custom graph-based approach to segment the surfaces of the swollen optic nerve head, the volumes of the nasal, superior, temporal, and inferior regions are computed. Using a dataset of 70 SD-OCT optic-nerve-head (ONH) SD-OCT scans the Spearman rank correlation coefficients between expert-defined Frisén scale grades and the total retinal (TR) volume, nasal, superior, temporal, inferior regional volumes were 0.737, 0.752, 0.747, 0.770 and 0.758, respectively. Also, a fuzzy k-nearest-neighbor (k-NN) algorithm was used to predict Frisén scale grades (in a leave-one-subject-out fashion). Using multiple features rather than just the TR volume made the resulting mean Frisén grade difference (MGD) between the expert-defined grades 0.386 (down from 0.629) and prediction accuracy 64.29% (up from 41.43%).
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