Classification of fundus autofluorescence patterns in early age-related macular disease

PURPOSE. To describe and classify patterns of abnormal fundus autofluorescence (FAF) in eyes with early nonexudative age-related macular disease (AMD). METHODS. FAF images were recorded in eyes with early AMD by confocal scanning laser ophthalmoscopy (cSLO) with excitation at 488 nm (argon or OPSL laser) and emission above 500 or 521 nm (barrier filter). A standardized protocol for image acquisition and generation of mean images after automated alignment was applied, and routine fundus photographs were obtained. FAF images were classified by two independent observers. The ? statistic was applied to assess intra- and interobserver variability. RESULTS. Alterations in FAF were classified into eight phenotypic patterns including normal, minimal change, focal increased, patchy, linear, lacelike, reticular, and speckled. Areas with abnormal increased or decreased FAF signals may or may not have corresponded to funduscopically visible alterations. For intraobserver variability, ? of observer I was 0.80 (95% confidence interval [CI]0.71-0.89) and of observer II, 0.74. (95% CI, 0.64-0.84). For interobserver variability, ? was 0.77 (95% CI, 0.67-0.87). CONCLUSIONS. Various phenotypic patterns of abnormal FAF can be identified with cSLO imaging. Distinct patterns may reflect heterogeneity at a cellular and molecular level in contrast to a nonspecific aging process. The results indicate that the classification system yields a relatively high degree of intra- and interobserver agreement. It may be applicable for determination of novel prognostic determinants in longitudinal natural history studies, for identification of genetic risk factors, and for monitoring of future therapeutic interventions to slow the progression of early AMD. Copyright © Association for Research in Vision and Ophthalmology.

Reversal of optic disc cupping after glaucoma surgery analyzed with a scanning laser tomograph

Objective: To detect and quantitate changes in optic nerve morphology after glaucoma surgery using the Heidelberg Retina Tomograph (HRT, Heidelberg Instruments, Heidelberg, Germany). Design: Nonconsecutive observational case series. Participants and Intervention: The authors prospectively enrolled 21 adult patients undergoing incisional glaucoma surgery for progressive glaucoma damage. Quantitative analysis of the optic nerve head by scanning laser tomography and automated perimetry were performed before and after glaucoma surgery. Main Outcome Measures: Changes in optic nerve parameters were subjected to linear regression analysis with respect to percent of postoperative reduction of intraocular pressure (IOP), as well as with respect to age, refraction, preoperative cup:disc ratio, and change in visual field parameters. Results: Seventeen patients had pre- and postoperative images suitable for analysis. Mean IOP at the time of image acquisition before surgery was 30.5 ± 12 mmHg, and after surgery 11.8 ± 5.2 mmHg (mean follow-up, 26 ± 7 weeks). Eleven of 13 (85%) patients having IOP reduction of greater than 40% showed improvement in optic disc parameters. All four patients with less than 25% reduction in IOP showed worsening of most parameters. Changes in optic disc parameters were highly correlated with percent IOP reduction and with age. The parameters in which change most strongly correlated with percent change of IOP were cup area, rim area, cup:disc ratio, and mean cup depth (each, P <0.005). The age of the patient correlated highly with change in maximum cup depth (P <0.005). Refraction and clinically determined cup:disc ratio correlated poorly with changes in measured optic disc parameters. Clinical improvement in visual fields was correlated with the degree of improvement of cup:disc ratio (P = 0.025). Conclusion: Most patients showing a 40% lowering of IOP after glaucoma surgery show improved optic nerve morphology as measured by the HRT. The amount of improvement correlated highly with the percent reduction of IOP.

Optimization of image quality and dose for Varian aS500 electronic portal imaging devices (EPIDs)

This study was carried out to investigate whether the electronic portal imaging (EPI) acquisition process could be optimized, and as a result tolerance and action levels be set for the PIPSPro QC-3V phantom image quality assessment. The aim of the optimization process was to reduce the dose delivered to the patient while maintaining a clinically acceptable image quality. This is of interest when images are acquired in addition to the planned patient treatment, rather than images being acquired using the treatment field during a patient's treatment. A series of phantoms were used to assess image quality for different acquisition settings relative to the baseline values obtained following acceptance testing. Eight Varian aS500 EPID systems on four matched Varian 600C/D linacs and four matched Varian 2100C/D linacs were compared for consistency of performance and images were acquired at the four main orthogonal gantry angles. Images were acquired using a 6 MV beam operating at 100 MU min(-1) and the low-dose acquisition mode. Doses used in the comparison were measured using a Farmer ionization chamber placed at d(max) in solid water. The results demonstrated that the number of reset frames did not have any influence on the image contrast, but the number of frame averages did. The expected increase in noise with corresponding decrease in contrast was also observed when reducing the number of frame averages. The optimal settings for the low-dose acquisition mode with respect to image quality and dose were found to be one reset frame and three frame averages. All patients at the Northern Ireland Cancer Centre are now imaged using one reset frame and three frame averages in the 6 MV 100 MU min(-1) low-dose acquisition mode. Routine EPID QC contrast tolerance (+/-10) and action (+/-20) levels using the PIPSPro phantom based around expected values of 190 (Varian 600C/D) and 225 (Varian 2100C/D) have been introduced. The dose at dmax from electronic portal imaging has been reduced by approximately 28%, and while the image quality has been reduced, the images produced are still clinically acceptable.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.