Techniques and Applications in Skin OCT Analysis.

The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.

Segregation of neuronal and vascular retinal damage in patients with hypertension and diabetes.

This study aimed to examine the impact of diabetes and hypertension on retinal nerve fiber layer (RNFL) thickness components. Optical coherence tomography (OCT) measurements do not consider blood vessel contribution, which this study addressed. We hypothesized that diabetes and/or hypertension would lead to thinner RNFL versus controls due to the vascular component. OCT angiography was used to measure the RNFL in 121 controls, 50 diabetes patients, 371 hypertension patients, and 177 diabetes patients with hypertension. A novel technique separated the RNFL thickness into original (vascular component) and corrected (no vascular component) measurements. Diabetes-only (98 ± 1.7 µm; p = 0.002) and diabetes with hypertension (99 ± 0.8 µm; p = 0.001) patients had thinner original RNFL versus controls (102 ± 0.8 µm). No difference was seen between hypertension-only patients (101 ± 0.5 µm; p = 0.083) and controls. After removing the blood vessel component, diabetes/hypertension groups had thinner corrected RNFL versus controls (p = 0.024). Discrepancies in diabetes/hypertension patients were due to thicker retinal blood vessels within the RNFL thickness (p = 0.002). Our findings suggest that diabetes and/or hypertension independently contribute to neurodegenerative thinning of the RNFL, even in the absence of retinopathy. The differentiation of neuronal and vascular components in RNFL thickness measurements provided by the novel technique highlights the importance of considering vascular changes in individuals with these conditions.

Neurovascular segregation of the retinal nerve fiber layer in glaucoma.

The imaging data of one eye from 154 healthy and 143 glaucoma participants were acquired to evaluate the contributions of the neuronal and vascular components within the retinal nerve fiber layer (RNFL) for detecting glaucoma and modeling visual field loss through the use of optical coherence tomography (OCT) and OCT angiography. The neuronal and vascular components within the circumpapillary RNFL were independently evaluated. In healthy eyes, the neuronal component showed a stronger association with age (r = -0.52, p < 0.001) compared to measured RNFL thickness (r = -0.46, p < 0.001). Using the neuronal component alone improved detection of glaucoma (AUC: 0.890 ± 0.020) compared to measured RNFL thickness (AUC: 0.877 ± 0.021; χ2 = 5.54, p = 0.019). Inclusion of the capillary components with the sectoral neuronal component resulted in a significant improvement in glaucoma detection (AUC: 0.927 ± 0.015; χ2 = 15.34, p < 0.001). After adjusting for potential confounders, AUC increased to 0.952 ± 0.011. Results from modeling visual field loss in glaucoma eyes suggest that visual field losses associated with neuronal thinning were moderated in eyes with a larger capillary component. These findings suggest that segregation of the neurovascular components could help improve understanding of disease pathophysiology and affect disease management in glaucoma.

Segregation of neuronal-vascular components in a retinal nerve fiber layer for thickness measurement using OCT and OCT angiography.

Assessment of the circumpapillary retinal nerve fiber layer (RNFL) provides crucial knowledge on the status of the optic nerve. Current circumpapillary RNFL measurements consider only thickness, but an accurate evaluation should also consider blood vessel contribution. Previous studies considered the presence of major vessels in RNFL thickness measurements from optical coherence tomography (OCT). However, such quantitative measurements do not account for smaller vessels, which could also affect circumpapillary RNFL measurements. We present an approach to automatically segregate the neuronal and vascular components in circumpapillary RNFL by combining vascular information from OCT angiography (OCTA) and structural data from OCT. Automated segmentation of the circumpapillary RNFL using a state-of-the-art deep learning network is first performed and followed by the lateral and depth-resolved localization of the vascular component by vertically projecting the vessels along the circular scan from OCTA vessels map onto the segmented RNFL. Using this proposed approach, we compare the correlations of circumpapillary RNFL thicknesses with age at different levels of vessel exclusion (exclusion of major vessels only vs both major- and micro-vessels) and also evaluate the thickness variability in 75 healthy eyes. Our results show that the ratio of major- and micro-vessels to circumpapillary RNFL achieved a stronger correlation with aging (r = 0.478, P < .001) than the ratio with only major vessels to circumpapillary RNFL (r = 0.027, P = .820). Exclusion of blood vessels from circumpapillary RNFL thickness using OCTA imaging provides a better measure of the neuronal components and could potentially improve the diagnostic performance for disease detection.

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images.

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.

Developing a normative database for retinal perfusion using optical coherence tomography angiography.

Visualizing and characterizing microvascular abnormalities with optical coherence tomography angiography (OCTA) has deepened our understanding of ocular diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Two types of microvascular defects can be detected by OCTA: focal decrease because of localized absence and collapse of retinal capillaries, which is referred to as the non-perfusion area in OCTA, and diffuse perfusion decrease usually detected by comparing with healthy case-control groups. Wider OCTA allows for insights into peripheral retinal vascularity, but the heterogeneous perfusion distribution from the macula, parapapillary area to periphery hurdles the quantitative assessment. A normative database for OCTA could estimate how much individual's data deviate from the normal range, and where the deviations locate. Here, we acquired OCTA images using a swept-source OCT system and a 12×12 mm protocol in healthy subjects. We automatically segmented the large blood vessels with U-Net, corrected for anatomical factors such as the relative position of fovea and disc, and segmented the capillaries by a moving window scheme. A total of 195 eyes were included and divided into 4 age groups: < 30 (n=24) years old, 30-49 (n=28) years old, 50-69 (n=109) years old and >69 (n=34) years old. This provides an age-dependent normative database for characterizing retinal perfusion abnormalities in 12×12 mm OCTA images. The usefulness of the normative database was tested on two pathological groups: one with diabetic retinopathy; the other with glaucoma.

Three-dimensional choroidal vessel network quantification using swept source optical coherence tomography.

Precise three-dimensional segmentation of choroidal vessels helps us understand the development and progression of multiple ocular diseases, such as agerelated macular degeneration and pathological myopia. Here we propose a novel automatic choroidal vessel segmentation framework for swept source optical coherence tomography (SS-OCT) to visualize and quantify three-dimensional choroidal vessel networks. Retinal pigment epithelium (RPE) was delineated from volumetric data and enface frames along the depth were extracted under the RPE. Choroidal vessels on the first enface frame were labeled by adaptive thresholding and each subsequent frame was segmented via segment propagation from the frame above and was in turn used as the reference for the next frame. Choroid boundary was determined by structural similarity index between adjacent frames. The framework was tested on 33 mm SS-OCT volumes acquired by a prototype SS-OCT system (PlexElite 9000, Zeiss Meditec, Dublin, CA, US), and vessel metrics including perfusion density, vessel density and mean vessel diameter were computed. Results from human subjects (N = 8) and non-human primates (N = 6) were summarized.Clinical Relevance- Accurate 3D choroid vessel segmentation can help clinicians better quantify blood perfusion which can lead to improved diagnosis and management of retinal eye diseases.

Volumetric Choroidal Segmentation Using Sequential Deep Learning Approach in High Myopia Subjects.

Many ocular diseases are associated with choroidal changes. Therefore, it is crucial to be able to segment the choroid to study its properties. Previous methods for choroidal segmentation have focused on single cross-sectional scans. Volumetric choroidal segmentation has yet to be widely reported. In this paper, we propose a sequential segmentation approach using a variation of U-Net with a bidirectional C-LSTM(Convolutional Long Short Term Memory) module in the bottleneck region. The model is evaluated on volumetric scans from 40 high myopia subjects, obtained using SS-OCT(Swept Source Optical Coherence Tomography). A comparison with other U-Net-based variants is also presented. The results demonstrate that volumetric segmentation of the choroid can be achieved with an accuracy of IoU(Intersection over Union) 0.92.Clinical relevance- This deep learning approach can automatically segment the choroidal volume, which can enable better evaluation and monitoring at ocular diseases.

Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT.

Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.

Localization of Anatomical Features in Vascular-enhanced Enface OCT Images.

Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance- Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.

Association Between Structure-function Characteristics and Visual Field Outcomes in Glaucoma Subjects With Intraocular Pressure Reduction After Trabeculectomy.

PRECIS: Improvements in post-trabeculectomy visual field (VF) outcomes were found to be significantly associated with preoperative nerve fiber layer thickness parameters extracted from the sectorized structure-function relationship, baseline VF, and severity of glaucoma. OBJECTIVE: To determine whether the preoperative structure-function relationship helps to predict visual outcomes at 1-year post-trabeculectomy. PATIENTS AND METHODS: In total, 91 eyes from 87 participants who successfully underwent trabeculectomy were included in our study. All eyes received optical coherence tomography imaging and VF assessment using 30-2 standard automated perimetry preoperatively at baseline and postoperatively 1 year after trabeculectomy. The linear mixed-model analysis was used to assess the association of structure and function at baseline, and multivariate analysis to investigate factors associated with postoperative VF outcomes. RESULTS: Results from multivariate and univariate analysis for VF 1 year after trabeculectomy showed that a positive preoperative retinal nerve fiber layer thickness deviation from the structure-function model was found to be significantly associated with improved postoperative VF outcomes [ß=0.06 dB/µm; 95% confidence interval (CI), 0.03-0.09]. Other significant factors included baseline VF MD (ß=-0.18; 95% CI, -0.23 to -0.13) and the presence of severe glaucoma (ß=-1.69; 95% CI, -2.80 to -0.57). Intraocular pressure was positively associated with improved VF outcomes only in univariate analysis (ß=0.06; 95% CI, 0.01-0.11). CONCLUSIONS AND RELEVANCE: Characteristics derived from the baseline structure-function relationship were found to be strongly associated with postoperative VF outcomes. These findings suggest that the structure-function relationship could potentially have a role in predicting VF progression after trabeculectomy.

Dissolving Triamcinolone-Embedded Microneedles for the Treatment of Keloids: A Single-Blinded Intra-Individual Controlled Clinical Trial.

INTRODUCTION: Keloids are a prevalent chronic skin disorder with significant psychosocial morbidity. Intralesional corticosteroid injections are the first-line treatment but are painful and require repeated injections by medical professionals. Dissolving microneedles are a novel method of cutaneous drug delivery that induces minimal/no pain and can be self-administered. The objective of the study was to evaluate the efficacy and safety of triamcinolone-embedded dissolving microneedles in treatment of keloids. METHODS: This was a single-blind, intra-individual controlled two-phase clinical trial of 8-week duration each. Two keloids per subject were selected for (1) once-daily 2-min application with microneedles for 4 weeks, followed by no treatment for the next 4 weeks, or (2) non-intervention as control. Primary outcome was change in keloid volume as assessed by a high-resolution 3D scanner. RESULTS: There was significant reduction in keloid volume compared with controls after 4 weeks of treatment. This reduction was greater with a higher dosage of triamcinolone used. CONCLUSIONS: Once-daily application of dissolving triamcinolone-embedded microneedles significantly reduced the volume of keloids. The treatment was safe, can be self-administered and can serve as an alternative for patients unsuitable for conventional treatments. TRIAL REGISTRATION: Trial Registry: Health Science Authority (Singapore) Clinical Trials Register Registration number: 2015/00440.

Automatic Detection of Preferred Retinal Locus (PRL) for Low Vision Rehabilitation using Oculometrics Analysis.

Low vision rehabilitation is an important step towards individuals with Age-related macular degeneration (AMD) regaining useful functional vision and quality of life. One of the key steps in low vision rehabilitation is the determination of the preferred retinal locus (PRL). Currently, localization of PRL is performed manually though the guidance of a low vision therapist and the process is highly time-consuming, labour-intensive and subjective. In this paper, we present an automated system to objectively and accurately locate an individual's PRL with the aid of gaze tracking technology. We also propose a graph-based method for false fixation detection to improve the system robustness. We validated the reliability of the system by using eye tracking technology to simulate central vision loss on people with normal vision. Experimental results show the potential use of eye tracking in detecting PRL for low vision rehabilitation.

Three-dimensional graph-based skin layer segmentation in optical coherence tomography images for roughness estimation.

Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.

Epidermal segmentation in high-definition optical coherence tomography.

Epidermis segmentation is a crucial step in many dermatological applications. Recently, high-definition optical coherence tomography (HD-OCT) has been developed and applied to imaging subsurface skin tissues. In this paper, a novel epidermis segmentation method using HD-OCT is proposed in which the epidermis is segmented by 3 steps: the weighted least square-based pre-processing, the graph-based skin surface detection and the local integral projection-based dermal-epidermal junction detection respectively. Using a dataset of five 3D volumes, we found that this method correlates well with the conventional method of manually marking out the epidermis. This method can therefore serve to effectively and rapidly delineate the epidermis for study and clinical management of skin diseases.

Automatic retinal interest evaluation system (ARIES).

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. However, in practice, retinal image quality is a big concern as automatic systems without consideration of degraded image quality will likely generate unreliable results. In this paper, an automatic retinal image quality assessment system (ARIES) is introduced to assess both image quality of the whole image and focal regions of interest. ARIES achieves 99.54% accuracy in distinguishing fundus images from other types of images through a retinal image identification step in a dataset of 35342 images. The system employs high level image quality measures (HIQM) to perform image quality assessment, and achieves areas under curve (AUCs) of 0.958 and 0.987 for whole image and optic disk region respectively in a testing dataset of 370 images. ARIES acts as a form of automatic quality control which ensures good quality images are used for processing, and can also be used to alert operators of poor quality images at the time of acquisition.