RETINAL HYPERREFLECTIVE FOCI IN TYPE 1 DIABETES MELLITUS.

PURPOSE: To investigate hyperreflective foci (HF) on spectral-domain optical coherence tomography in patients with Type 1 diabetes mellitus across different stages of diabetic retinopathy (DR) and diabetic macular edema (DME) and to study clinical and morphological characteristics associated with HF. METHODS: Spectral-domain optical coherence tomography scans and color fundus photographs were obtained of 260 patients. Spectral-domain optical coherence tomography scans were graded for the number of HF and other morphological characteristics. The distribution of HF across different stages of DR and DME severity were studied. Linear mixed-model analysis was used to study associations between the number of HF and clinical and morphological parameters. RESULTS: Higher numbers of HF were found in patients with either stage of DME versus patients without DME (P < 0.001). A trend was observed between increasing numbers of HF and DR severity, although significance was only reached for moderate nonproliferative DR (P = 0.001) and proliferative DR (P = 0.019). Higher numbers of HF were associated with longer diabetes duration (P = 0.029), lower high-density lipoprotein cholesterol (P = 0.005), and the presence of microalbuminuria (P = 0.005). In addition, HF were associated with morphological characteristics on spectral-domain optical coherence tomography, including central retinal thickness (P = 0.004), cysts (P < 0.001), subretinal fluid (P = 0.001), and disruption of the external limiting membrane (P = 0.018). CONCLUSION: The number of HF was associated with different stages of DR and DME severity. The associations between HF and clinical and morphological characteristics can be of use in further studies evaluating the role of HF as a biomarker for disease progression and treatment response.

Morphological and topographical appearance of microaneurysms on optical coherence tomography angiography.

AIMS: To investigate retinal microaneurysms in patients with diabetic macular oedema (DME) by optical coherence tomography angiography (OCTA) according to their location and morphology in relationship to their clinical properties, leakage on fundus fluorescein angiography (FFA) and retinal thickening on structural OCT. METHODS: OCTA and FFA images of 31 eyes of 24 subjects were graded for the presence of microaneurysms. The topographical and morphological appearance of microaneurysms on OCTA was evaluated and classified. For each microaneurysm, the presence of focal leakage on FFA and associated retinal thickening on OCT was determined. RESULTS: Of all microaneurysms flagged on FFA, 295 out of 513 (58%) were also visible on OCTA. Microaneurysms with focal leakage and located in a thickened retinal area were more likely to be detected on OCTA than not leaking microaneurysms in non-thickened retinal areas (p=0.001). Most microaneurysms on OCTA were seen in the intermediate (23%) and deep capillary plexus (22%). Of all microaneurysms visualised on OCTA, saccular microaneurysms were detected most often (31%), as opposed to pedunculated microaneurysms (9%). Irregular, fusiform and mixed fusiform/saccular-shaped microaneurysms had the highest likeliness to leak and to be located in thickened retinal areas (p<0.001, p<0.001 and p=0.001). CONCLUSIONS: Retinal microaneurysms in DME could be classified topographically and morphologically by OCTA. OCTA detected less microaneurysms than FFA, and this appeared to be dependent on leakage activity and retinal thickening. Morphological appearance of microaneurysms (irregular, fusiform and mixed saccular/fusiform) was associated with increased leakage activity and retinal thickening.

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

Automatic detection of the foveal center in optical coherence tomography.

We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 µm, with a mean (± SD) distance of 71 µm ± 107 µm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 µm ± 84 µm and 56 µm ± 80 µm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.

Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks.

We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.

Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography.

Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans. Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans. Results: The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively). Conclusions: A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.