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.

In cases in which it is suspected that polyhydramnios is due to anomalies of the digestive tract, the absence of fluid in the fetal gastrointestinal tract suggests the diagnosis of esophageal atresia. This diagnosis can be made by observing the alternating filling and emptying of the esophagus proximal to the site of atresia. CASE 1A 28-year-old woman gravida 111, para 11, was referred to our hospital because of a uterus too big for dates, which by palpation was 37-weeks size. The ultrasound scan showed polyhydramnios and a biparietal diameter (BPD) of 86 mm, equivalent to 33 weeks, which corresponded to her last menstrual period. Absence of fluid in the stomach and intestine ( Fig. 1) was noted in the fetus. As the study was performed with real-time and recorded on video tape, an alternating filling and emptying of a large proximal esophagus was seen (Figs. 2 and 3), suggesting esophageal atresia Type I as the most probable diagnosis (Type I Ladd's classification: esophageal atresia without tracheoesophageal fistula). 'I2 The umbilical cord contained two vessels and the alpha feto protein in amniotic fluid was just above the upper limit of normal. No other fetal abnormality was found.Spontaneous vaginal delivery took place 5 days after the ultrasound study was performed, resulting in the birth of a female baby weighing 2120 g. After birth the esophageal atresia was clinically confirmed and a thoracoabdominal radiograph showed a distended gastric chamber with air and a gasless intestine (Fig. 4). This finding changed the diagnosis made before delivery from Type I esophageal atresia to esophageal atresia with

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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.

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.

Optical coherence tomography (OCT) and high-frequency ultrasound (HFUS), two established imaging modalities in the field of dermatology, were evaluated and compared regarding their applicability for visualization of skin tissue morphology and quantification of murine intradermal structures. The accuracy and reproducibility of both methods were assessed ex vivo and in vivo using a standardized model for intradermal volumes based on injected soft tissue fillers. OCT revealed greater detail in skin morphology, allowing for detection of single layers due to the superior resolution. Volumetric data measured by OCT (7.9 ± 0.3 μl) and HFUS (7.7 ± 0.5 μl) were in good agreement and revealed a high accuracy when compared to the injected volume of 7.98 ± 0.8 µl. In vivo, OCT provided a higher precision (relative SD: 26% OCT vs. 42% HFUS) for the quantification of intradermal structures, whereas HFUS offered increased penetration depth enabling the visualization of deeper structures. A combination of both imaging technologies might be valuable for tumor assessments or other dermal pathologies in clinical settings.

We present vessel density alterations in response to flicker stimulation using optical coherence tomography angiography and identified the superficial capillary plexus as the layer with the most pronounced effect. This points out the physiological importance of the microvasculature in mediating functional hyperemia and suggests a fine-tuned plexus-specific mechanism to meet cellular metabolic demands.

Vascular changes and alterations of oxygen metabolism are suggested to be implicated in multiple sclerosis (MS) pathogenesis and progression. Recently developed in vivo retinal fundus imaging technologies provide now an opportunity to non-invasively assess metabolic changes in the neural retina. This study was performed to assess retinal oxygen metabolism, peripapillary capillary density (CD), large vessel density (LVD), retinal nerve fiber layer thickness (RNFLT) and ganglion cell inner plexiform layer thickness (GCIPLT) in patients with diagnosed relapsing multiple sclerosis (RMS) and history of unilateral optic neuritis (ON). 16 RMS patients and 18 healthy controls (HC) were included in this study. Retinal oxygen extraction was modeled using O2 saturations and Doppler optical coherence tomography (DOCT) derived retinal blood flow (RBF) data. CD and LVD were assessed using optical coherence tomography (OCT) angiography. RNFLT and GCIPLT were measured using structural OCT. Measurements were performed in eyes with (MS+ON) and without (MS-ON) history for ON in RMS patients and in one eye in HC. Total oxygen extraction was lowest in MS+ON (1.8 ± 0.2 μl O2/min), higher in MS-ON (2.1 ± 0.5 μl O2/min, p = 0.019 vs. MS+ON) and highest in HC eyes (2.3 ± 0.6 μl O2/min, p = 0.002 vs. MS, ANOVA p = 0.031). RBF was lower in MS+ON (33.2 ± 6.0 μl/min) compared to MS-ON (38.3 ± 4.6 μl/min, p = 0.005 vs. MS+ON) and HC eyes (37.2 ± 4.7 μl/min, p = 0.014 vs. MS+ON, ANOVA p = 0.010). CD, LVD, RNFLT and GCIPL were significantly lower in MS+ON eyes. The present data suggest that structural alterations in the retina of RMS patients are accompanied by changes in oxygen metabolism, which are more pronounced in MS+ON than in MS-ON eyes. Whether these alterations promote MS onset and progression or occur as consequence of disease warrants further investigation.Clinical Trial Registration:ClinicalTrials.gov registry, NCT03401879.