Deep learning has dramatically improved object recognition, speech recognition, medical image analysis and many other fields. Optical coherence tomography (OCT) has become a standard of care imaging modality for ophthalmology. We asked whether deep learning could be used to segment cornea OCT images. Using a custom-built ultrahigh-resolution OCT system, we scanned 72 healthy eyes and 70 keratoconic eyes. In total, 20,160 images were labeled and used for the training in a supervised learning approach. A custom neural network architecture called CorneaNet was designed and trained. Our results show that CorneaNet is able to segment both healthy and keratoconus images with high accuracy (validation accuracy: 99.56%). Thickness maps of the three main corneal layers (epithelium, Bowman's layer and stroma) were generated both in healthy subjects and subjects suffering from keratoconus. CorneaNet is more than 50 times faster than our previous algorithm. Our results show that deep learning algorithms can be used for OCT image segmentation and could be applied in various clinical settings. In particular, CorneaNet could be used for early detection of keratoconus and more generally to study other diseases altering corneal morphology.
The aim of this mechanistic clinical study was to explore the effect of water-free perfluorohexyloctane eye drops on tear film thickness (TFT) in patients with dry eye disease (DED). Methods: Forty-eight patients with mild to moderate DED participated in this randomized, single-masked, observer-blinded parallel group study in a 1:1 ratio to receive either perfluorohexyloctane or unpreserved 0.9% saline solution (Hydrabak Ò , Thea, France) eye drops 4 times daily in both eyes for 4 weeks. A custom-built ultrahigh-resolution optical coherence tomography system was used to measure TFT. Furthermore, evaluation of lipid layer thickness (LLT) and noninvasive tear film breakup time, as well as standard clinical tests for signs and symptoms of DED were performed. Results: Mean TFT and LLT at baseline were comparable between the 2 treatment groups. After a single drop instillation, perfluorohexyloctane eye drops temporarily increased TFT immediately. After multiple dosing, perfluorohexyloctane eye drops gradually increased TFT over time with a maximum effect at the end of the study (least square mean difference: 6.42%; P = 0.0142 at week 4). LLT values measured before drop instillation showed a more prominent increase in LLT for perfluorohexyloctane eye drops (13.36%-26.33% vs. 3.21%-28.65%). All other parameters got better in both treatment groups with no statistical difference between groups. Conclusions: These results demonstrate that perfluorohexyloctane eye drops increase TFT as well as LLT over time. These tear film reestablishing attributes are in line with the mode of action of perfluorohexyloctane eye drops to avoid evaporation through stabilization of the lipid layer.
The tear meniscus contains most of the tear fluid and therefore is a good indicator for the state of the tear film. Previously, we used a custom-built optical coherence tomography (OCT) system to study the lower tear meniscus by automatically segmenting the image data with a thresholding-based segmentation algorithm (TBSA). In this report, we investigate whether the results of this image segmentation algorithm are suitable to train a neural network in order to obtain similar or better segmentation results with shorter processing times. Considering the class imbalance problem, we compare two approaches, one directly segmenting the tear meniscus (DSA), the other first localizing the region of interest and then segmenting within the higher resolution image section (LSA). A total of 6658 images labeled by the TBSA were used to train deep convolutional neural networks with supervised learning. Five-fold cross-validation reveals a sensitivity of 96.36% and 96.43%, a specificity of 99.98% and 99.86% and a Jaccard index of 93.24% and 93.16% for the DSA and LSA, respectively. Average segmentation times are up to 228 times faster than the TBSA. Additionally, we report the behavior of the DSA and LSA in cases challenging for the TBSA and further test the applicability to measurements acquired with a commercially available OCT system. The application of deep learning for the segmentation of the tear meniscus provides a powerful tool for the assessment of the tear film, supporting studies for the investigation of the pathophysiology of dry eye-related diseases.
Photoreceptor function is impaired in many retinal diseases like age-related macular degeneration. Currently, assessment of the photoreceptor function for the early diagnosis and monitoring of these diseases is either subjective, as in visual field testing, requires contact with the eye, like in electroretinography, or relies on research prototypes with acquisition speeds unattained by conventional imaging systems. We developed an objective, noncontact method to monitor photoreceptor function using a standard optical coherence tomography system. This method can be used with various white light sources for stimulation. The technique was applied in five volunteers and detected a decrease of volume of the subretinal space associated with light adaptation processes of the retina.
Many different parameters exist for the investigation of tear film dynamics. We present a new tear meniscus segmentation algorithm which automatically extracts tear meniscus area (TMA), height (TMH), depth (TMD) and radius (TMR) from UHR-OCT measurements and apply it to a data set including repeated measurements from ten healthy subjects. Mean values and standard deviations are 0.0174 ± 0.007 mm 2 , 0.272 ± 0.069 mm, 0.191 ± 0.049 mm and 0.309 ± 0.123 mm for TMA, TMH, TMD and TMR, respectively. A significant correlation was found between all respective tear meniscus parameter pairs (all p < 0.001, all Pearson's r ≥ 0.657). Challenges, limitations and potential improvements related to the data acquisition and the algorithm itself are discussed. The automatic segmentation of tear meniscus measurements acquired with UHR-OCT might help in a clinical setting to further understand the tear film and related medical conditions like dry eye disease.
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