The Association between Tear Film Thickness as Measured with OCT and Symptoms and Signs of Dry Eye Disease: A Pooled Analysis of 6 Clinical Trials.

PURPOSE: To determine the association between tear film thickness (TFT) as measured with ultra-high resolution optical coherence tomography (UHR-OCT) and signs and symptoms of dry eye disease (DED). METHODS: A total of 450 eyes from 225 patients with DED from six different randomized clinical trials were included in this pooled analysis. In all subjects, TFT was measured with a custom-built UHR-OCT system. Symptoms of DED were quantified using a standardized Ocular Surface Disease Index (OSD)I questionnaire and clinical signs including tear film break up time (TFBUT) and Schirmer I test were assessed. Associations of the average TFT with OSDI, TFBUT, and Schirmer I test were calculated using a linear regression analysis. RESULTS: The average TFT of the included sample (mean age, 45.0 ± 13.3 years; 65% female) was 4.2 ± 0.5 µm and the OSDI 36.2 ± 10.4. A significant negative correlation was found between TFT and OSDI (r = -0.36 to -0.31; p < 0.001). Tear break up time and Schirmer I test were not correlated with OSDI. Significant albeit weak correlations were found between TFT and TFBUT (r = 0.17 to 0.25; p < 0.01) as well as Schirmer I (r = 0.36 to 0.37; p < 0.001). Subgroup analysis revealed that the correlation was stronger in the subjects with abnormal Schirmer I (<15 mm; r = 0.50 to 0.54; p < 0.001). CONCLUSIONS: The present study demonstrates an objective measurement of TFT using a novel OCT approach for DED that correlates with symptoms and signs of DED. Our data are consistent with the idea that TFT represents the aqueous-deficient component of DED.

Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus.

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.

In vivo corneal endothelium imaging using ultrahigh resolution OCT.

We investigate the influence of optical coherence tomography (OCT) system resolution on high-quality in vivo en face corneal endothelial cell images of the monkey eye, to allow for quantitative analysis of cell density. We vary the lateral resolution of the ultrahigh resolution (UHR) OCT system (centered at 850 nm) by using different objectives, and the axial resolution by windowing the source spectrum. By suppressing the motion of the animal, we are able to obtain a high-quality en face corneal endothelial cell map in vivo using UHR OCT for the first time with a lateral resolution of 3.1 µm. Increasing lateral resolution did not result in a better image quality but a smaller field of view (FOV), and the axial resolution had little impact on the visualization of corneal endothelial cells. Quantitative analysis of cell density was performed on in vivo en face OCT images of corneal endothelial cells, and the results are in agreement with previously reported data. Our study may offer a practical guideline for designing OCT systems that allow for in vivo corneal endothelial cell imaging with high quality.

Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks.

We present a system for automatic determination of the intradermal volume of hydrogels based on optical coherence tomography (OCT) and deep learning. Volumetric image data was acquired using a custom-built OCT prototype that employs an akinetic swept laser at ~1310 nm with a bandwidth of 87 nm, providing an axial resolution of ~6.5 µm in tissue. Three-dimensional data sets of a 10 mm × 10 mm skin patch comprising the intradermal filler and the surrounding tissue were acquired. A convolutional neural network using a u-net-like architecture was trained from slices of 100 OCT volume data sets where the dermal filler volume was manually annotated. Using six-fold cross-validation, a mean accuracy of 0.9938 and a Jaccard similarity coefficient of 0.879 were achieved.

CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning.

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.

[Cornea Imaging by Optical Coherence Tomography - Historical Aspects and Most Recent Technical Developments]. / Bildgebung in der Kornea mittels optischer Kohärenztomografie ­ historische Entwicklung und neueste technische Fortschritte.

Since its introduction for retinal imaging in the early 1990s, optical coherence tomography (OCT) has undergone rapid development and has revolutionised ophthalmology. Although OCT was initially used mainly for the examination of the retina, numerous systems for the assessment of the anterior segment of the eye have now been developed. OCT is based on the detection and processing of the light back-scattered and back-reflected by the tissue, and provides completely new possibilities for the ophthalmologist to examine the structures of the anterior eye segment. Depending on the technical implementation, OCT systems for the anterior eye allow precise measurement of individual layers of the cornea and the chamber angle or - in the form of ultrahigh resolution OCT - the detailed visualisation of corneal morphology with near histological resolution. Through further technical developments, especially with respect to an increase in acquisition speeds, OCT has become an essential tool in the differential diagnosis and follow-up of various diseases of the cornea and might also provide new insights into their pathophysiology.

Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT.

In optical coherence tomography (OCT), the axial resolution is directly linked to the coherence length of the employed light source. It is currently unclear if OCT allows measuring thicknesses below its axial resolution value. To investigate spectral-domain OCT imaging in the super-resolution regime, we derived a signal model and compared it with the experiment. Several island thin film samples of known refractive indices and thicknesses in the range 46 - 163 nm were fabricated and imaged. Reference thickness measurements were performed using a commercial atomic force microscope. In vivo measurements of the tear film were performed in 4 healthy subjects. Our results show that quantitative super-resolved thickness measurement can be performed using OCT. In addition, we report repeatable tear film lipid layer visualization. Our results provide a novel interpretation of the OCT axial resolution limit and open a perspective to deeper extraction of the information hidden in the coherence volume.

Tear film thickness after treatment with artificial tears in patients with moderate dry eye disease.

PURPOSE: This study was designed to investigate the effect of a single-drop instillation of different lacrimal substitutes on tear film thickness (TFT) assessed with optical coherence tomography in patients with mild to moderate dry eye disease. METHODS: The study was performed in a randomized, double-masked, controlled parallel group design. Patients received a single dose of either unpreserved trehalose 30 mg/mL and sodium hyaluronate 1.5 mg/mL (TH-SH, Thealoz Duo), unpreserved sodium hyaluronate, 0.15% (HA, Hyabak) or sodium chloride, 0.9% (NaCl, Hydrabak) eye drops. Sixty patients finished the study according to the protocol. TFT was measured with a custom-built ultrahigh-resolution Fourier domain optical coherence tomography system providing a resolution of 1.2 µm. RESULTS: The mean TFT before treatment was 2.5 ± 0.4 µm. Ten minutes after instillation, TFT significantly increased in the TH-SH group from 2.4 ± 0.4 to 3.1 ± 0.9 µm (P < 0.01) and in the HA group from 2.4 ± 0.3 to 2.9 ± 0.5 µm (P < 0.01), whereas no significant change was observed in the NaCl group (from 2.6 ± 0.4 to 2.7 ± 0.4 µm, P = 0.76). The increase in TFT remained statistically significant up to 240 minutes after administration of TH-SH. In contrast, the increase in TFT after administration of HA was only statistically significant at 10, 20, and 40 minutes after drop instillation. CONCLUSIONS: The findings of this study indicate that single instillation of TH-SH and HA eye drops increases TFT in patients with dry eye disease. The data also indicate longer corneal residence of the TH-containing eye drops. The effect of multiple instillation and long-term use of artificial tears on TFT warrants further investigation.