1,378 research outputs found
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Retina layer segmentation using kernel graph cuts and continuous max-flow
Circular scan Spectral-Domain Optic Coherence Tomography imaging (SD-OCT) is one of the best tools for diagnosis of retinal diseases. This technique provides more comprehensive detail of the retinal morphology and layers around the optic disc nerve head (ONH). Since manual labelling of the retinal layers can be tedious and time consuming, accurate and robust automated segmentation methods are needed to provide the thickness evaluation of these layers in retinal disorder assessments such as glaucoma. The proposed method serves this purpose by performing the segmentation of retinal layers boundaries in circular SD-OCT scans acquired around the ONH. The layers are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. Results from scan images acquired with Spectralis (Heidelberg Engineering, Germany) prove that the proposed method is robust and efficient in detecting the retinal layers boundaries in images. With a mean root-mean-square error (RMSE) of 0.0835 ± 0.0495 and an average Dice coefficient of 0.9468 ± 0.0705 pixels for the retinal nerve fibre layer thickness, the proposed method demonstrated effective agreement with manual annotations
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Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images
Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation
Simultaneous Multiple Surface Segmentation Using Deep Learning
The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical imaging applications. Despite their widespread use, these require human
experts to design transformations, image features, surface smoothness priors,
and re-design for a different tissue, organ or imaging modality. Here, we
propose a Deep Learning based approach for segmentation of the surfaces in
volumetric medical images, by learning the essential features and
transformations from training data, without any human expert intervention. We
employ a regional approach to learn the local surface profiles. The proposed
approach was evaluated on simultaneous intraretinal layer segmentation of
optical coherence tomography (OCT) images of normal retinas and retinas
affected by age related macular degeneration (AMD). The proposed approach was
validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The
experiments showed statistically significant improvement in accuracy for our
approach compared to state-of-the-art graph based optimal surface segmentation
with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used
to learn the surfaces for both normal and diseased images. The mean unsigned
surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI
2.02-2.60 voxels) was improved to voxels (95% CI 1.14-1.40 voxels) using
our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB
memory, which is much faster than the 2837.46 s and 6.87 GB memory required by
the G-OSC method on the same computer system.Comment: 8 page
Monitoring retinal changes with optical coherence tomography predicts neuronal loss in experimental autoimmune encephalomyelitis.
BACKGROUND:Retinal optical coherence tomography (OCT) is a clinical and research tool in multiple sclerosis, where it has shown significant retinal nerve fiber (RNFL) and ganglion cell (RGC) layer thinning, while postmortem studies have reported RGC loss. Although retinal pathology in experimental autoimmune encephalomyelitis (EAE) has been described, comparative OCT studies among EAE models are scarce. Furthermore, the best practices for the implementation of OCT in the EAE lab, especially with afoveate animals like rodents, remain undefined. We aimed to describe the dynamics of retinal injury in different mouse EAE models and outline the optimal experimental conditions, scan protocols, and analysis methods, comparing these to histology to confirm the pathological underpinnings. METHODS:Using spectral-domain OCT, we analyzed the test-retest and the inter-rater reliability of volume, peripapillary, and combined horizontal and vertical line scans. We then monitored the thickness of the retinal layers in different EAE models: in wild-type (WT) C57Bl/6J mice immunized with myelin oligodendrocyte glycoprotein peptide (MOG35-55) or with bovine myelin basic protein (MBP), in TCR2D2 mice immunized with MOG35-55, and in SJL/J mice immunized with myelin proteolipid lipoprotein (PLP139-151). Strain-matched control mice were sham-immunized. RGC density was counted on retinal flatmounts at the end of each experiment. RESULTS:Volume scans centered on the optic disc showed the best reliability. Retinal changes during EAE were localized in the inner retinal layers (IRLs, the combination of the RNFL and the ganglion cell plus the inner plexiform layers). In WT, MOG35-55 EAE, progressive thinning of IRL started rapidly after EAE onset, with 1/3 of total loss occurring during the initial 2 months. IRL thinning was associated with the degree of RGC loss and the severity of EAE. Sham-immunized SJL/J mice showed progressive IRL atrophy, which was accentuated in PLP-immunized mice. MOG35-55-immunized TCR2D2 mice showed severe EAE and retinal thinning. MBP immunization led to very mild disease without significant retinopathy. CONCLUSIONS:Retinal neuroaxonal damage develops quickly during EAE. Changes in retinal thickness mirror neuronal loss and clinical severity. Monitoring of the IRL thickness after immunization against MOG35-55 in C57Bl/6J mice seems the most convenient model to study retinal neurodegeneration in EAE
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Computational models for stuctural analysis of retinal images
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts
such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis
technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in
the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the
mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut
with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results
of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results
obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd
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