Vitreoretinal interface changes in geographic atrophy.

PURPOSE: Geographic atrophy (GA) is the end-stage manifestation of atrophic age-related macular degeneration (AMD). The disease progresses slowly over time, eventually causing loss of central vision. Its cause and pathomechanism are not fully known. Previous studies have suggested that vitreoretinal traction (VRT) may contribute to the progression of neovascular AMD. The aim of this study was to examine whether an association between changes at the vitreoretinal interface (VRI), in particular traction (VRT), and the characteristics and progression of GA in eyes with dry AMD can be established. DESIGN: Clinic-based prospective cohort study. PARTICIPANTS: A total of 97 patients (age range, 61-90 years; mean, 78.4 years) with GA secondary to dry AMD were enrolled. Patients exhibiting neovascular signs on fluorescein angiography in either eye were excluded. METHODS: The VRI changes were examined using spectral-domain optical coherence tomography (SD-OCT). Characteristics of GA were examined using fundus autofluorescence (FAF) imaging. All imaging was performed using a Spectralis SLO+OCT device (Heidelberg Engineering, Heidelberg, Germany); GA area was measured using the Region Finder (Heidelberg Engineering) software native to the Spectralis platform. MAIN OUTCOME MEASURES: Area and increase in area of GA. RESULTS: A total of 97 eyes were examined. Vitreoretinal traction was found in 39 eyes (40%). The GA area at baseline was 6.65±5.64 mm(2) in eyes with VRT and 5.73±4.72 mm(2) in eyes with no VRT. The annual rate of progression of GA area progression was 2.99±0.66 mm(2) in eyes with VRT and 1.45±0.67mm(2) in eyes without VRT. Differences between groups in both parameters were statistically significant (n = 97 total number of eyes; P<0.001). Multiple regression analysis confirmed this finding (B = 0.714, P<0.001; F3,93 = 72.542, P<0.001; adjusted R(2) = 0.691) CONCLUSIONS: Our results indicate an association between VRT and an increased rate of progression of GA area in dry AMD. Monitoring VRT may contribute to an improved estimate of the prospective time of visual loss and to a better timing of emerging therapies in dry AMD.

Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints.

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 µm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Pathology hinting as the combination of automatic segmentation with a statistical shape model.

With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.