Ocular microvascular complications in diabetic retinopathy: insights from machine learning

INTRODUCTION

Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study that included optical coherence tomography angiography (OCTA) images collected from a healthy group (196 eyes), a DM no DR group (120 eyes), a mild DR group (71 eyes), and a moderate DR group (66 eyes). We implemented a non-parametric machine learning method for four classification tasks that used parameters extracted from the OCTA images as predictors: DM no DR versus healthy, mild DR versus DM no DR, moderate DR versus mild DR, and any DR versus no DR. SHapley Additive exPlanations values were used to determine the importance of these parameters in the classification.

RESULTS

We found large choriocapillaris flow deficits were the most important for healthy versus DM no DR, and became less important in eyes with mild or moderate DR. The superficial microvasculature was important for the healthy versus DM no DR and mild DR versus moderate DR tasks, but not for the DM no DR versus mild DR task-the stage when deep microvasculature plays an important role. Foveal avascular zone metric was in general less affected, but its involvement increased with worsening DR.

CONCLUSIONS

The findings from this study provide valuable insights into the microvascular involvement of DM and DR, facilitating the development of early detection methods and intervention strategies.

Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography

AIMS

To validate a deep learning (DL) algorithm (DLA) for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan).

METHODS

This was a reliability analysis from a cross-sectional study. An independent test set of 39 936 SS-OCT scans from 312 phakic subjects (128 SS-OCT meridional scans per eye) was analysed. Participants above 50 years with no previous history of intraocular surgery were consecutively recruited from glaucoma clinics. Indentation gonioscopy and dark room SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle. For each subject, all images were analysed by a DL-based network based on the VGG-16 architecture, for gonioscopic angle-closure detection. Area under receiver operating characteristic curves (AUCs) and other diagnostic performance indicators were calculated for the DLA (index test) against gonioscopy (reference standard).

RESULTS

Approximately 80% of the participants were Chinese, and more than half were women (57.4%). The prevalence of gonioscopic angle closure in this hospital-based sample was 20.2%. After analysing a total of 39 936 SS-OCT scans, the AUC of the DLA was 0.85 (95% CI:0.80 to 0.90, with sensitivity of 83% and a specificity of 87%) to classify gonioscopic angle closure with the optimal cut-off value of >35% of circumferential angle closure.

CONCLUSIONS

The DLA exhibited good diagnostic performance for detection of gonioscopic angle closure on 360° SS-OCT scans in a glaucoma clinic setting. Such an algorithm, independent of the identification of the scleral spur, may be the foundation for a non-contact, fast and reproducible 'automated gonioscopy' in future.

Longitudinal Structural and Microvascular Observation in RCS Rat Eyes Using Optical Coherence Tomography Angiography

Purpose

To evaluate the change of retinal thickness and ocular microvasculature in a rat model of retinitis pigmentosa using swept source optical coherence tomography angiography (SS-OCTA)

Methods

Three-weeks-old Royal College of Surgeons (RCS) rats (n = 8) and age-matched control rats (n = 14) were imaged by a prototype SS-OCTA system. Follow-up measurements occurred every three weeks on six RCS rats until week 18, and cross-sectional measurements were conducted on control rats. Thicknesses of different retinal layers and the total retina were measured. The enface angiograms from superficial vascular plexiform (SVP) and deep capillary plexiform (DCP) were analyzed, and the image sharpness was also extracted from the choroidal angiograms. Immunohistochemical analysis was done in the RCS rats after week 18, as well as in three-week-old RCS rats and age-matched controls.

Results

In RCS rats, the thicknesses of the ganglion cell complex, the nuclear layer, the debris/photoreceptor layer and the total retina decreased over the weeks (P < 0.001). The SVP metrics remained unchanged whereas the DCP metrics decreased significantly over the weeks (P < 0.001). The immunohistochemical analysis confirmed our OCTA findings of capillary dropout in the DCP. The choroidal plexus appeared indistinct initially due to scattering of light at the intact retinal pigment epithelium (RPE) and became more visible after week nine probably due to RPE degeneration. Loss of choriocapillaris was visualized at week 18. In control rats, no vascular change was detected, but nuclear layers, photoreceptor layers and total retina showed slight thinning with age (P < 0.001).

Conclusions

Photoreceptor degeneration in RCS rats was associated with the loss of capillaries in DCP, but not in SVP. The OCTA imaging allows for the characterization of structural and angiographic changes in rodent models.

Approaches to quantify optical coherence tomography angiography metrics

Optical coherence tomography (OCT) has revolutionized the field of ophthalmology in the last three decades. As an OCT extension, OCT angiography (OCTA) utilizes a fast OCT system to detect motion contrast in ocular tissue and provides a three-dimensional representation of the ocular vasculature in a non-invasive, dye-free manner. The first OCT machine equipped with OCTA function was approved by U.S. Food and Drug Administration in 2016 and now it is widely applied in clinics. To date, numerous methods have been developed to aid OCTA interpretation and quantification. In this review, we focused on the workflow of OCTA-based interpretation, beginning from the generation of the OCTA images using signal decorrelation, which we divided into intensity-based, phase-based and phasor-based methods. We further discussed methods used to address image artifacts that are commonly observed in clinical settings, to the algorithms for image enhancement, binarization, and OCTA metrics extraction. We believe a better grasp of these technical aspects of OCTA will enhance the understanding of the technology and its potential application in disease diagnosis and management. Moreover, future studies will also explore the use of ocular OCTA as a window to link ocular vasculature to the function of other organs such as the kidney and brain.

Association of Antihypertensive Medication with Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness

PURPOSE

To evaluate the association between different classes of antihypertensive medication with retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness in a nonglaucomatous multiethnic Asian population.

DESIGN

Population-based, cross-sectional study.

PARTICIPANTS

A total of 9144 eyes for RNFL analysis (2668 Malays, 3554 Indians, and 2922 Chinese) and 8549 eyes for GC-IPL analysis (2460 Malays, 3230 Indians, and 2859 Chinese) aged 44 to 86 years.

METHODS

Participants underwent standardized systemic and ocular examinations and interviewer-administered questionnaires for collection of data on medication and other variables. Intraocular pressure (IOP) readings were obtained by Goldmann applanation tonometry before pupil dilation for fundoscopy and OCT imaging. Blood pressure (BP) was measured with an automatic BP monitor. Mean arterial pressure (MAP) was defined as diastolic BP plus 1/3 (systolic BP - diastolic BP). Regression models were used to investigate the association of antihypertensive medication with OCT measurements of RNFL and GC-IPL.

MAIN OUTCOME MEASURES

Average and sectoral RNFL and GC-IPL thickness.

RESULTS

After adjusting for age, gender, ethnicity, MAP, IOP, body mass index (BMI), and presence of diabetes, we found that participants taking any type of antihypertensive medication (β = -0.83; 95% confidence interval [CI], -1.46 to -0.02; P = 0.01), specifically angiotensin-converting enzyme inhibitors (ACEIs) (β = -1.66; 95% CI, -2.57 to -0.75; P < 0.001) or diuretics (β = -1.38; 95% CI, -2.59 to -0.17; P < 0.05), had thinner average RNFL in comparison with participants who were not receiving antihypertensive treatment. Use of a greater number of antihypertensive medications was significantly associated with thinner average RNFL (P for trend = 0.001). This association was most evident in the inferior RNFL quadrant in participants using ACEIs (β = -2.44; 95% CI, -3.99 to -0.89; P = 0.002) or diuretics (β = -2.76; 95% CI, -4.76 to -0.76; P = 0.007). A similar trend was noted in our analysis of macular GC-IPL thickness.

CONCLUSIONS

Use of 2 or more antihypertensive medications, ACEI, and diuretics were associated with a loss of structural markers of retinal ganglion cell health in a multiethnic Asian population.

Automatic Glaucoma Detection from Stereo Fundus Images

This paper proposes a new method for automatic detection of glaucoma from stereo pair of fundus images. The basis for detecting glaucoma is using the optic cup-to-disc area ratio, where the surface area of the optic cup is segmented from the disparity map estimated from the stereo fundus image pair. More specifically, we first estimate the disparity map from the stereo image pair. Then, the optic disc is segmented from one of the stereo image. Based upon the location of the optic disc, we perform an active contour segmentation on the disparity map to segment the optic cup. Thereafter, we can compute the optic cup-to-disc area ratio by dividing the area (i.e. the total number of pixels) of the segmented optic cup region to that of the segmented optic disc region. Our experimental results using the available test dataset shows the efficacy of our proposed approach.

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.

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.

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.

Comparison of retinal vessel diameter measurements from swept-source OCT angiography and adaptive optics ophthalmoscope

BACKGROUND/IMS

To compare the retinal vessel diameter measurements obtained from the swept-source optical coherence tomography angiography (OCTA; Plex Elite 9000, Carl Zeiss Meditec, USA) and adaptive optics ophthalmoscope (AOO; RTX1, Imagine Eyes, France).

METHODS

Fifteen healthy subjects, 67% women, mean age (SD) 30.87 (6.19) years, were imaged using OCTA and AOO by a single experienced operator on the same day. Each eye was scanned using two OCTA protocols (3×3 mm² and 9×9 mm²) and two to five AOO scans (1.2×1.2 mm²). The OCTA and AOO scans were scaled to the same pixel resolution. Two independent graders measured the vessel diameter at the same location on the region-of-interest in the three coregistered scans. Differences in vessel diameter measurements between the scans were assessed.

RESULTS

The inter-rater agreement was excellent for vessel diameter measurement in both OCTA protocols (ICC=0.92) and AOO (ICC=0.98). The measured vessel diameter was widest from the OCTA 3×3 mm² (55.2±16.3 µm), followed by OCTA 9×9 mm² (54.7±14.3 µm) and narrowest by the AOO (50.5±15.6 µm; p<0.001). Measurements obtained from both OCTA protocols were significantly wider than the AOO scan (OCTA 3×3 mm²: mean difference Δ=4.7 µm, p<0.001; OCTA 9×9 mm²: Δ=4.2 µm, p<0.001). For vessels >45 µm, it appeared to be larger in OCTA 3×3 mm² scan than the 9×9 mm² scan (Δ=1.9 µm; p=0.005), while vessels <45 µm appeared smaller in OCTA 3×3 mm² scan (Δ=-1.3 µm; p=0.009) CONCLUSIONS: The diameter of retinal vessels measured from OCTA scans were generally wider than that obtained from AOO scans. Different OCTA scan protocols may affect the vessel diameter measurements. This needs to be considered when OCTA measures such as vessel density are calculated.

Quantitative Microvascular Analysis With Wide-Field Optical Coherence Tomography Angiography in Eyes With Diabetic Retinopathy

IMPORTANCE

Wide-field optical coherence tomographic angiography (OCTA) may provide insights to peripheral capillary dropout in eyes with diabetic retinopathy (DR).

OBJECTIVE

To describe the diagnostic performance of wide-field OCTA with and without large vessel removal for assessment of DR in persons with diabetes.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study was performed from April 26, 2018, to April 8, 2019, at a single tertiary eye center in Singapore. Case patients were those with type 2 diabetes for more than 5 years and bilateral DR diagnosed by fundus imaging; control participants included those with no self-reported history of diabetes, a fasting glucose level within the normal range in the past year, and no ocular pathologic findings. A wide-field (12 × 12-mm2) fovea-centered scan was performed using a prototype swept source OCTA system. Retinal microvasculature was examined by separating the angiograms into large vessels, capillaries, and capillary dropout regions.

MAIN OUTCOMES AND MEASURES

Area under the receiver operating characteristic curve (AUC) for DR severity discrimination using wide-field vascular metrics. Retinal perfusion density (RPD), capillary perfusion density (CPD), large vessel density (LVD), and capillary dropout density (CDD) were calculated. Low-contrast regions were excluded from the calculation.

RESULTS

A total of 49 eyes in 27 control participants (17 male [63.0%]; mean [SD] age, 59.96 [7.63] years; age range, 44-79 years) and 76 eyes in 47 patients with diabetes (29 male [61.7%]; mean [SD] age, 64.36 [8.08] years; range, 41-79 years) were included. Among eyes in patients with diabetes, 23 were in those with diabetes but no DR, 25 in those with mild nonproliferative DR, and 28 in those with moderate to severe nonproliferative DR. There was no difference in RPD, CPD, LVD, and CDD between the control group and the group with diabetes and no DR. There was a stepwise decrease in RPD, CPD, and CDD in the diabetes with no DR, mild nonproliferative DR, and moderate to severe nonproliferative DR groups, whereas LVD was not associated with DR staging. The nonproliferative DR group had decreased RPD, CPD, and CDD compared with the control group. The CPD had higher AUCs than RPD for discriminating diabetes with nonproliferative DR (combined mild and moderate to severe nonproliferative DR) vs no DM (AUC, 0.92 [95% CI, 0.87-0.98] vs 0.89 [95% CI, 0.83-0.95], P = .01), diabetes with no DR vs mild nonproliferative DR (AUC, 0.81 [95% CI, 0.68-0.94] vs 0.77 [95% CI, 0.64-0.91], P = .18), and mild nonproliferative DR vs moderate to severe nonproliferative DR (AUC, 0.82 [95% CI, 0.71-0.94] vs 0.78 [95% CI, 0.65-0.91], P = .01) but similar AUCs for no DM vs diabetes with no DR. The total perfusion density and CPD in wide-field OCTA had better discriminative power than the central 6 × 6-mm2 field (CPD, 0.89 [95% CI, 0.83-0.95] vs 0.84 [95% CI, 0.77-0.92], P = .06; total perfusion density, 0.93 [95% CI, 0.87-0.98] vs 0.90 [95% CI, 0.83-0.96], P = .06).

CONCLUSIONS AND RELEVANCE

The findings suggest that wide-field OCTA provides information on microvascular perfusion and may be useful for detecting predominant peripheral capillary dropout in eyes with nonproliferative DR. A vascular selectivity approach excluding the large vessels may improve the discriminative power for different stages of DR.

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.

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.

Comparison of a commercial spectral-domain OCT and swept-source OCT based on an angiography scan for measuring circumpapillary retinal nerve fibre layer thickness

Background/aims

To assess the agreement in measuring retinal nerve fibre layer (RNFL) thickness between spectral-domain (SD; Cirrus HD, Carl Zeiss Meditec, USA) optical coherence tomography (OCT) and swept-source (SS; Plex Elite 9000, Carl Zeiss Meditec) OCT using an OCT angiography (OCTA) scanning protocol.

Methods

57 participants (12 glaucomatous, 8 ocular hypertensive and 74 normal eyes) were scanned with two OCT instruments by a single experienced operator on the same day. Circumpapillary RNFL thicknesses were automatically segmented for SD-OCT and manually segmented for SS-OCTA scans. Agreement of global RNFL thickness, as well as average thickness in four quadrants was assessed using intraclass correlation coefficients (ICCs).

Results

There was excellent agreement in the inferior and superior quadrants and the global (all ICC >0.90), followed by good agreement in the temporal (ICC=0.79) and nasal (ICC=0.73) quadrants. The ICC values were similar in the subgroups except within the ocular hypertension group, where the nasal quadrant was less agreeable (ICC=0.31). SS-OCTA-derived RNFL thickness was on average 3 µm thicker than SD-OCT, particularly in the nasal (69.7±11.5 µm vs 66.3±9.3 µm; p<0.001) and temporal (75.6±13.7 µm vs 67.9±12.3 µm; p<0.001) quadrants.

Conclusions

RNFL measurements taken with SS-OCTA have good-to-excellent agreement with SD-OCT, which suggests that the RNFL thickness can be sufficiently extracted from wide-field OCTA scans.

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.

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.

Glaucoma classification from retina optical coherence tomography angiogram

This paper presents a new method for classification of retina into glaucoma and non-glaucoma cases based on optical coherence tomography angiogram (OCTA). The key idea here is to analyze the retinal microvasculature in the optic disc area of an enface OCTA for glaucoma classification. To facilitate this analysis, we propose a way to extract a so-called "optic disc microvasculature" region and then propose several features that will be extracted from this microvasculature region. A machine classifier is then trained using the designated features and subsequently used to classify the OCTA data. We show that our proposed approach works well on the tested dataset.

Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels

BACKGROUND AND OBJECTIVES

Diabetic Retinopathy is the leading cause of blindness in developed countries in the age group 20-74 years. It is characterized by lesions on the retina and this paper focuses on detecting two of these lesions, Microaneurysms and Hemorrhages, which are also known as red lesions. This paper attempts to deal with two problems in detecting red lesions from retinal fundus images: (1) false detections on blood vessels; and (2) different size of red lesions.

METHODS

To deal with false detections on blood vessels, novel filters have been proposed which can distinguish between red lesions and blood vessels. This distinction is based on the fact that vessels are elongated while red lesions are usually circular blob-like structures. The second problem of the different size of lesions is dealt with by applying the proposed filters on patches of different sizes instead of filtering the full image. These patches are obtained by dividing the original image using a grid whose size determines the patch size. Different grid sizes were used and lesion detection results for these grid sizes were combined using Multiple Kernel Learning.

RESULTS

Experiments on a dataset of 143 images showed that proposed filters detected Microaneurysms and Hemorrhages successfully even when these lesions were close to blood vessels. In addition, using Multiple Kernel Learning improved the results when compared to using a grid of one size only. The areas under receiver operating characteristic curve were found to be 0.97 and 0.92 for Microaneurysms and Hemorrhages respectively which are better than the existing related works.

CONCLUSIONS

Proposed filters are robust to the presence of blood vessels and surpass related works in detecting red lesions from retinal fundus images. Improved lesion detection using the proposed approach can help in automatic detection of Diabetic Retinopathy.

An automatic quantitative measurement method for performance assessment of retina image registration algorithms

This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature. To compute edge dissimilarity score, we propose an edge dissimilarity measure that we called "robustified Hausdorff distance". We show that our proposed approach is feasible as designed by drawing comparison to visual evaluation results when tested on images from the DRIVERA and G9 dataset.

An augmented reality assistance platform for eye laser surgery

This paper presents a novel augmented reality assistance platform for eye laser surgery. The aims of the proposed system are for the application of assisting eye doctors in pre-planning as well as providing guidance and protection during laser surgery. We developed algorithms to automatically register multi-modal images, detect macula and optic disc regions, and demarcate these as protected areas from laser surgery. The doctor will then be able to plan the laser treatment pre-surgery using the registered images and segmented regions. Thereafter, during live surgery, the system will automatically register and track the slit lamp video frames on the registered retina images, send appropriate warning when the laser is near protected areas, and disable the laser function when it points into the protected areas. The proposed system prototype can help doctors to speed up laser surgery with confidence without fearing that they may unintentionally fire laser in the protected areas.

Red lesion detection in retinal fundus images using Frangi-based filters

This paper presents a method to detect red lesions related to Diabetic Retinopathy (DR), namely Microaneurysms and Hemorrhages from retinal fundus images with robustness to the presence of blood vessels. Filters based on Frangi filters are used for the first time for this task. Green channel of the input image was decomposed into smaller sub images and proposed filters were applied to each sub image after initial preprocessing. Features were extracted from the filter response and used to train a Support Vector Machine classifier to predict whether a test image had lesions or not. Experiments were performed on a dataset of 143 retinal fundus and the proposed method achieved areas under the ROC curve equal to 0.97 and 0.87 for Microaneurysms and Hemorrhages respectively. Results show the effectiveness of the proposed method for detecting red lesions. This method can help significantly in automated detection of DR with fewer false positives.

Automatic nuclear cataract grading using image gradients

This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.

An ensembling approach for optic cup detection based on spatial heuristic analysis in retinal fundus images

Optic cup detection remains a challenging task in retinal image analysis, and is of particular importance for glaucoma evaluation, where disease severity is often assessed by the size of the optic cup. In this paper, we propose spatial heuristic ensembling (SHE), an approach which aims to fuse the advantages of each method based on the specific performance in each defined sector. In this way, we generate an ensembled optic cup which is obtained from the optimal combination of the component methods. We conduct experiments on the ORIGA data set of 650 retinal images and show that the ensemble approach performs better than the individual segmentations, reducing the relative overlap error, and CDR errors by as much as 0.04 CDR units. The results are promising for the continued development of such an approach for improving optic cup segmentation.

Validity of a new optic disc grading software for use in clinical and epidemiological research

BACKGROUND

To determine the reliability and agreement of a new optic disc grading software program for use in clinical, epidemiological research.

DESIGN

Reliability and agreement study.

SAMPLES

328 monoscopic and 85 stereoscopic optic disc images.

METHODS

Optic disc parameters were measured using a new optic disc grading software (Singapore Optic Disc Assessment) that is based on polynomial curve-fitting algorithm. Two graders independently graded 328 monoscopic images to determine intergrader reliability. One grader regraded the images after 1 month to determine intragrader reliability. In addition, 85 stereo optic disc images were separately selected, and vertical cup-to-disc ratios were measured using both the new software and standardized Wisconsin manual stereo-grading method by the same grader 1 month apart. Intraclass correlation coefficient (ICC) and Bland-Altman plot analyses were performed.

MAIN OUTCOME MEASURES

Optic disc parameters.

RESULTS

The intragrader and intergrader reliability for optic disc measurements using Singapore Optic Disc Assessment was high (ICC ranging from 0.82 to 0.94). The mean differences (95% limits of agreement) for intergrader vertical cup-to-disc ratio measurements were 0.00 (-0.12 to 0.13) and 0.03 (-0.15 to 0.09), respectively. The vertical cup-to-disc ratio agreement between the software and Wisconsin grading method was extremely close (ICC = 0.94). The mean difference (95% limits of agreement) of vertical cup-to-disc ratio measurement between the two methods was 0.03 (-0.09 to 0.16).

CONCLUSIONS

Intragrader and intergrader reliability using Singapore Optic Disc Assessment was excellent. This software was highly comparable with standardized stereo-grading method. Singapore Optic Disc Assessment is useful for grading digital optic disc images in clinical, population-based studies.

Automatic detection of the macula in retinal fundus images using seeded mode tracking approach

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

Sector-based optic cup segmentation with intensity and blood vessel priors

The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA(-light) database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis.

Model-based optic nerve head segmentation on retinal fundus images

The optic nerve head (optic disc) plays an important role in the diagnosis of retinal diseases. Automatic localization and segmentation of the optic disc is critical towards a good computer-aided diagnosis (CAD) system. In this paper, we propose a method that combines edge detection, the Circular Hough Transform and a statistical deformable model to detect the optic disc from retinal fundus images. The algorithm was evaluated against a data set of 325 digital color fundus images, which includes both normal images and images with various pathologies. The result shows that the average error in area overlap is 11.3% and the average absolute area error is 10.8%, which outperforms existing methods. The result indicates a high correlation with ground truth segmentation and thus demonstrates a good potential for this system to be integrated with other retinal CAD systems.

Automatic glaucoma diagnosis from fundus image

Glaucoma is currently diagnosed by glaucoma specialists using specialized imaging devices like HRT and OCT. Fundus imaging is a modality widely used in primary healthcare. An automatic glaucoma diagnosis system based on fundus image can be deployed to primary healthcare clinics and has potential for early disease diagnosis. A mass glaucoma screening program can also be facilitated using such a system. We present an automatic fundus image based cup-to-disc ratio measurement system; and demonstrate its potential for automatic objective glaucoma diagnosis and screening. It provides strong support to use fundus image as the modality for automatic glaucoma diagnosis.

Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI

Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.

Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods

OBJECTIVE

Automatic tumour segmentation and volumetry is useful in cancer staging and treatment outcome assessment. This paper presents a performance benchmarking study on liver tumour segmentation for three semiautomatic algorithms: 2D region growing with knowledge-based constraints (A1), 2D voxel classification with propagational learning (A2) and Bayesian rule-based 3D region growing (A3).

METHODS

CT data from 30 patients were studied, and 47 liver tumours were isolated and manually segmented by experts to obtain the reference standard. Four datasets with ten tumours were used for algorithm training and the remaining 37 tumours for testing. Three evaluation metrics, relative absolute volume difference (RAVD), volumetric overlap error (VOE) and average symmetric surface distance (ASSD), were computed based on computerised and reference segmentations.

RESULTS

A1, A2 and A3 obtained mean/median RAVD scores of 17.93/10.53%, 17.92/9.61% and 34.74/28.75%, mean/median VOEs of 30.47/26.79%, 25.70/22.64% and 39.95/38.54%, and mean/median ASSDs of 2.05/1.41 mm, 1.57/1.15 mm and 4.12/3.41 mm, respectively. For each metric, we obtained significantly lower values of A1 and A2 than A3 (P < 0.01), suggesting that A1 and A2 outperformed A3.

CONCLUSIONS

Compared with the reference standard, the overall performance of A1 and A2 is promising. Further development and validation is necessary before reliable tumour segmentation and volumetry can be widely used clinically.

Towards automatic detection of age-related macular degeneration in retinal fundus images

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. The disease is highly associated with age, and becoming increasingly prevalent in our aging societies. Drusen is a pathological feature that is well-associated with AMD. In this paper, we present a method of detecting drusen in retinal fundus images. The method first determines the location of the macula, which is used as a landmark for a clinical drusen grading overlay. Subsequently, regions of drusen are identified though a maximal region-based pixel intensity approach via RGB and HSV channels. Methods of reducing the effect of retinal and choroidal vessels are also described. The system is tested on a sample set of 16 fundus images from a clinical study, with half having drusen. Experiments on the results show a sensitivity and specificity of 0.75 on the test image set.

Giant retinal tear management: an Asian experience

PURPOSE

To report the management of giant retinal tears (GRT).

METHODS

Retrospective review of all patients who underwent surgery for GRT with at least 6 months follow-up.

RESULTS

A total of 124 patients with 128 eyes were managed for GRT between 1991 and 2005. Of them, 99 eyes of 96 patients with at least 6 months follow-up were reviewed (mean follow-up 63.38 months). There were 113 men and the mean age was 39.6 years (8-72 years). Myopia was present in 81 eyes (63.3%) with mean myopia -7.56 dioptres sphere, DS (-1.00 to -27.00 DS). All eyes underwent pars plana vitrectomy. Scleral buckling was done in 90 eyes (70.3%) and lens removal in 49 of 95 phakic eyes (51.2%). Total 84 eyes (84.8%) were re-attached at 6 months after vitrectomy. This was achieved with one surgery in 71 eyes (71.7%). The mean number of surgeries for reattachment was 1.19 surgeries. At 6 months, 41 eyes (41.4%) regained 20/40 or better vision. A total of 47 vitreoretinal pathologies were seen in the fellow eyes of the 124 patients, including 4 with GRTs.

CONCLUSION

Surgical success for GRT can be achieved with good visual outcome in 84.8% after a mean of 1.19 surgeries. The fellow eyes are however at risk of pathologies and should be followed-up in the long term.

Redistribution of the zero order by the use of a phase checkerboard pattern in computer generated holograms

A checkerboard phase plate is proposed to be used together with computer generated holograms to eliminate the zero order by working as a convolution function that shifts the zero order away from the center of a reconstructed pattern. By performing a preshift in the desired hologram pattern, it is possible to obtain a reconstructed pattern that is free of zero order. Simulation results have shown that the technique is tolerant of fabrication errors in the hologram. The technique is also shown to effectively reduce the zero order intensity by two orders in the presence of phase depth errors in the checkerboard. Experimental results using a spatial light modulator support the results shown in the simulation.

Optimization of spot pattern in indoor diffuse optical wireless local area networks

In this paper, a simulated annealing (SA) algorithm is proposed to be used in the optimization of the spot pattern for the indoor diffuse optical wireless network application. The channel response is analyzed using conventional grid-based patterns and a field of view (FOV) of 30 degrees is found to give a good performance balance in the uniformity of the received power distribution and multipath dispersion. Using the algorithm to determine the spot pattern for the minimum standard deviation of the received power, an improvement of more than 85% is realized. To optimize the spot pattern at 30 degrees FOV, a merit function is incorporated into the algorithm for two parameters, and the SA algorithm is run to obtain optimized spot patterns for both a 4.5m and 6m extent of the spot pattern. Various weights are used, and a performance improvement of 39% and 78% is observed for the 4.5m and 6m spot pattern sizes respectively which shows that the approach can be used to effectively optimize the spot pattern in the indoor optical wireless application.