CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA).

This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo, shape, loop and anastomoses) in OCTA images. Methods: This retrospective and cross-sectional study includes 130 OCTA images from 101 patients with treatment-naïve CNV. At baseline, OCTA volumes of 6 × 6 mm2 were obtained to develop an AI-based algorithm to evaluate the CNV activity based on five activity criteria, including tiny branching vessels, anastomoses and loops, peripheral arcades, and perilesional hypointense halos. The proposed algorithm comprises two steps. The first block includes the pre-processing and segmentation of CNVs in OCTA images using a modified U-Net network. The second block consists of five binary classification networks, each implemented with various models from scratch, and using transfer learning from pre-trained networks. Results: The proposed segmentation network yielded an averaged Dice coefficient of 0.86. The individual classifiers corresponding to the five activity criteria (branch, peripheral arcade, dark halo, shape, loop, and anastomoses) showed accuracies of 0.84, 0.81, 0.86, 0.85, and 0.82, respectively. The AI-based algorithm potentially allows the reliable detection and segmentation of CNV from OCTA alone, without the need for imaging with contrast agents. The evaluation of the activity criteria in CNV lesions obtains acceptable results, and this algorithm could enable the objective, repeatable assessment of CNV features.

Peripheral Flicker Fusion at High Luminance: Beyond the Ferry-Porter Law.

The relationship between luminous intensity and the maximum frequency of flicker that can be detected defines the limits of the temporal-resolving ability of the human visual system, and characterizing it has important theoretical and practical applications; particularly for determining the optimal refresh rate for visual displays that would avoid the visibility of flicker and other temporal artifacts. Previous research has shown that this relationship is best described by the Ferry-Porter law, which states that critical flicker fusion (CFF) increases as a linear function of log retinal illuminance. The existing experimental data showed that this law holds for a wide range of stimuli and up to 10,000 Trolands; however, beyond this, it was not clear if the CFF continued to increase linearly or if the function saturated. Our aim was to extend the experimental data available to higher light intensities than previously reported in the literature. For this, we measured the peripheral CFF at a range of illuminances over six orders of magnitude. Our results showed that for up to 104 Trolands, the data conformed to the Ferry-Porter law with a similar slope, as previously established for this eccentricity; however, at higher intensities, the CFF function flattens and saturates at ~90 Hz for a target size of 5.7 degrees, and at ~100 Hz for a target of 10 degrees of angular size. These experimental results could prove valuable for the design of brighter visual displays and illumination sources that are temporally modulated.

Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases.

Purpose: Optical coherence tomography (OCT) has recently emerged as a source for powerful biomarkers in neurodegenerative diseases such as multiple sclerosis (MS) and neuromyelitis optica (NMO). The application of machine learning techniques to the analysis of OCT data has enabled automatic extraction of information with potential to aid the timely diagnosis of neurodegenerative diseases. These algorithms require large amounts of labeled data, but few such OCT data sets are available now. Methods: To address this challenge, here we propose a synthetic data generation method yielding a tailored augmentation of three-dimensional (3D) OCT data and preserving differences between control and disease data. A 3D active shape model is used to produce synthetic retinal layer boundaries, simulating data from healthy controls (HCs) as well as from patients with MS or NMO. Results: To evaluate the generated data, retinal thickness maps are extracted and evaluated under a broad range of quality metrics. The results show that the proposed model can generate realistic-appearing synthetic maps. Quantitatively, the image histograms of the synthetic thickness maps agree with the real thickness maps, and the cross-correlations between synthetic and real maps are also high. Finally, we use the generated data as an augmentation technique to train stronger diagnostic models than those using only the real data. Conclusions: This approach provides valuable data augmentation, which can help overcome key bottlenecks of limited data. Translational Relevance: By addressing the challenge posed by limited data, the proposed method helps apply machine learning methods to diagnose neurodegenerative diseases from retinal imaging.