Choroidal imaging by spectral domain-optical coherence tomography

AbstractDespite the fact that the choroid plays an important role in the structure and function of the eye, it has not been studied in detail in vivo. Improvements in optical coherence tomography (OCT) imaging technology allow the routine imaging of the choroid and deep optic nerve structures in most patients. As with any new technology, it needs validation in both healthy and diseased eyes. Reproducible measurements of choroidal and lamina cribrosa thickness are possible. Several variables such as age, axial length, and time of day, affect choroidal thickness and must be taken into account when interpreting data on choroidal thickness. Lamina cribrosa thickness appears to be affected by age as well but other factors need to be determined. Choroidal thickness may be used to differentiate between central serous chorioretinopathy (CSC), polypoidal choroidal vasculopathy (PCV) and exudative age-related macular degeneration (AMD). Enhanced depth imaging-optical coherence tomography (EDI-OCT) of the choroid may detect tumors not detectable by ultrasound. Studying the choroid may help us gain insight into the pathogenesis of several diseases such as AMD, CSC, glaucoma, posteriorly located choroidal tumors, and PCV among others

Automatic Choroidal Layer Segmentation Using Markov Random Field And Level Set Method

The choroid is an important vascular layer that supplies oxygen and nourishment to the retina. The changes in thickness of the choroid have been hypothesised to relate to a number of retinal diseases in the pathophysiology. In this work, an automatic method is proposed for segmenting the choroidal layer from macular images by using the level set framework. The 3D nonlinear anisotropic diffusion filter is used to remove all the OCT imaging artifacts including the speckle noise and to enhance the contrast. The distance regularisation and edge constraint terms are embedded into the level set method to avoid the irregular and small regions and keep information about the boundary between the choroid and sclera. Besides, the Markov Random Field method models the region term into the framework by correlating the single pixel likelihood function with neighbour-hood information to compensate for the inhomogeneous texture and avoid the leakage due to the shadows cast by the blood vessels during imaging process. The effectiveness of this method is demonstrated by comparing against other segmentation methods on a dataset with manually labelled ground truth. The results show that our method can successfully and accurately estimate the posterior choroidal boundary

Level set segmentation of retinal structures

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Changes in retinal structure are related to different eye diseases. Various retinal imaging techniques, such as fundus imaging and optical coherence tomography (OCT) imaging modalities, have been developed for non-intrusive ophthalmology diagnoses according to the vasculature changes. However, it is time consuming or even impossible for ophthalmologists to manually label all the retinal structures from fundus images and OCT images. Therefore, computer aided diagnosis system for retinal imaging plays an important role in the assessment of ophthalmologic diseases and cardiovascular disorders. The aim of this PhD thesis is to develop segmentation methods to extract clinically useful information from these retinal images, which are acquired from different imaging modalities. In other words, we built the segmentation methods to extract important structures from both 2D fundus images and 3D OCT images. In the first part of my PhD project, two novel level set based methods were proposed for detecting the blood vessels and optic discs from fundus images. The first one integrates Chan-Vese's energy minimizing active contour method with the edge constraint term and Gaussian Mixture Model based term for blood vessels segmentation, while the second method combines the edge constraint term, the distance regularisation term and the shape-prior term for locating the optic disc. Both methods include the pre-processing stage, used for removing noise and enhancing the contrast between the object and the background. Three automated layer segmentation methods were built for segmenting intra-retinal layers from 3D OCT macular and optic nerve head images in the second part of my PhD project. The first two methods combine different methods according to the data characteristics. First, eight boundaries of the intra-retinal layers were detected from the 3D OCT macular images and the thickness maps of the seven layers were produced. Second, four boundaries of the intra-retinal layers were located from 3D optic nerve head images and the thickness maps of the Retinal Nerve Fiber Layer (RNFL) were plotted. Finally, the choroidal layer segmentation method based on the Level Set framework was designed, which embedded with the distance regularisation term, edge constraint term and Markov Random Field modelled region term. The thickness map of the choroidal layer was calculated and shown.Department of Computer Science, Brunel University London

An in vivo investigation of choroidal vasculature in age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of visual impairment in the developed world. Whilst the pathogenesis is complex and not fully understood, changes to the choroidal vasculature in AMD have been demonstrated using histology. Advances in imaging technology, particularly long-wavelength optical coherence tomography (OCT), allow in vivo visualisation and investigation of this structure. The aim of this work is to determine whether changes to the choroidal vasculature are detectable in AMD using in vivo imaging. This was achieved through the evaluation of parameters for quantifying the structure, and the application of a machine learning approach to automated disease severity classification, based on choroidal appearance. Participants with early AMD (n=25), neovascular AMD (nAMD; n=25), and healthy controls (n=25) underwent imaging with a non-commercial long-wavelength (λc=1040 nm) OCT device. Subfoveal choroidal thickness, choroidal area, and luminal area were significantly lower in the nAMD group than the healthy and early AMD groups, whilst vessel ratio was significantly greater (P<0.05 in all cases). There was no significant difference in visible vessel diameter, choroidal vascularity index, luminal area ratio, or luminal perimeter ratio between the groups. No significant differences were found between the healthy and early AMD groups for any of the eight vascular parameters assessed. Classification of the disease groups based on choroidal OCT images was demonstrated using machine learning techniques. Textural features within the images were extracted using Gabor filters, and K-nearest neighbour, support vector machine, and random forest classifiers were assessed for this classification task. Textural changes were most pronounced in late-stage disease, although attribution to pathology or pharmacological intervention (anti-VEGF treatment) was not possible. Changes were also discernible in the early AMD group, suggesting sensitivity of this approach to detecting vascular involvement in early disease. In conclusion, structural changes to the choroidal vasculature in AMD are detectable in vivo using OCT imaging, demonstrated with both manual and automated analysis techniques. Whilst changes were most prominent in late-stage disease, subtle structural changes in early AMD were identified with texture analysis, warranting further investigation to improve our understanding of choroidal involvement in the pathogenesis of early AMD

Eye as a window to the brain: investigating the clinical utility of retinal imaging derived biomarkers in the phenotyping of neurodegenerative disease.

Background Neurodegenerative diseases, like multiple sclerosis, dementia and motor neurone disease, represent one of the major public health threats of our time. There is a clear persistent need for novel, affordable, and patient‐acceptable biomarkers of these diseases, to assist with diagnosis, prognosis and impact of interventions. And these biomarkers need to be sensitive, specific and precise. The retina is an attractive site for exploring this potential, as it is easily accessible to non‐invasive imaging. Remarkable technology revolutions in retinal imaging are enabling us to see the retina in microscopic level detail, and measure neuronal and vascular integrity. Aims and objectives I therefore propose that retinal imaging could provide reliable and accurate markers of these neurological diseases. In this project, I aimed to explore the clinical utility of retinal imaging derived measures of retinal neuronal and vessel size and morphology, and determine their candidacy for being reliable biomarkers in these diseases. I also aimed to detail the methods of retinal imaging acquisition, and processing, and the principles underlying all these stages, in relation to understanding of retinal structure and function. This provides an essential foundation to the application of retinal imaging analysis, highlighting both the strengths and potential weaknesses of retinal biomarkers and how they are interpreted. Methods After performing detailed systematic reviews and meta‐analyses of the existing work on retinal biomarkers of neurodegenerative disease, I carried out a prospective, controlled, cross‐sectional study of retinal image analysis, in patients with MS, dementia, and ALS. This involved developing new software for vessel analysis, to add value and maximise the data available from patient imaging episodes. Results From the systematic reviews, I identified key unanswered questions relating to the detailed analysis and utility of neuroretinal markers, and diseases with no studies yet performed of retinal biomarkers, such as non‐AD dementias. I recruited and imaged 961 participants over a two‐year period, and found clear patterns of significance in the phenotyping of MS, dementia and ALS. Detailed analysis has provided new insights into how the retina may yield important disease information for the individual patient, and also generate new hypotheses with relation to the disease pathophysiology itself. Conclusions Overall, the results show that retinal imaging derived biomarkers have an important and specific role in the phenotyping of neurodegenerative diseases, and support the hypothesis that the eye is an important window to neurological brain disease

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally