Microaneurysm detection using deep learning and interleaved freezing

Diabetes affects one in eleven adults. Diabetic retinopathy is a microvascular complication of diabetes and the leading cause of blindness in the working-age population. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper proposes an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network for detection of microaneurysms is proposed. Compared to other methods that require five processing stages, it requires only two. Furthermore, a novel network fine-tuning scheme called Interleaved Freezing is presented. This procedure significantly reduces the amount of time needed to re-train a network and produces competitive results. The proposed method was evaluated using publicly available and widely used datasets: E-Ophtha and ROC. It outperforms the state-of-the-art methods in terms of free-response receiver operatic characteristic (FROC) metric. Simplicity, performance, efficiency and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications

Exudate segmentation using fully convolutional neural networks and inception modules

Diabetic retinopathy is an eye disease associated with diabetes mellitus and also it is the leading cause of preventable blindness in working-age population. Early detection and treatment of DR is essential to prevent vision loss. Exudates are one of the earliest signs of diabetic retinopathy. This paper proposes an automatic method for the detection and segmentation of exudates in fundus photographies. A novel fully convolutional neural network architecture with Inception modules is proposed. Compared to other methods it does not require the removal of other anatomical structures. Furthermore, a transfer learning approach is applied between small datasets of different modalities from the same domain. To the best of authors’ knowledge, it is the first time that such approach has been used in the exudate segmentation domain. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed algorithm accomplished better results using a diseased/not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications

Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy

Purpose: Several studies have established, using various measurement modalities, that progression from diabetes to diabetic retinopathy is associated with changes in haemodynamics or measurable vascular geometry. In this study we take vessel measurements from standard fundus images, and estimate haemodynamic parameters (which are not directly observable) using a simple haemodynamic model. We show that there are statistically significant changes in some estimated haemodynamic parameters associated with the development of DR. Methods: A longitudinal study of twenty-four subjects was conducted. For each subject four fundus images were used, taken annually during the three years before the appearance of DR and in the first year of DR. A venous and arterial vascular bifurcation, each of which consisted of a parent vessel and two child branches was extracted, and at the branching nodes a zero dimensional model estimated the fluid dynamic conditions in terms of volumetric blood flow, blood flow velocity, nodal pressure, wall shear stress and Reynolds number. These features were statistically analyzed using linear mixed models. Results: A number of parameters, primarily venous, showed significant change with the development of DR, including early change two years before the onset of DR. A large proportion of overall variance is accounted for by individual patient differences, making progressive study essential. Conclusion: This is the first paper to demonstrate that haemodynamic feature estimates extracted from standard fundus images are sensitive to progression from diabetes to DR. In our future work, we aim to test whether the variations in haemodynamic conditions are predictive of progression prior to the appearance of retinal lesions

A fluid-dynamic based approach to reconnect the retinal vessels in fundus photography

This paper introduces the use of fluid-dynamic modeling to determine the connectivity of overlapping venous and arterial vessels in fundus images. Analysis of the retinal vascular network may provide information related to systemic and local disorders. However, the automated identification of the vascular trees in retinal images is a challenging task due to the low signal-to-noise ratio, nonuniform illumination and the fact that fundus photography is a projection on to the imaging plane of three-dimensional retinal tissue. A zero-dimensional model was created to estimate the hemodynamic status of candidate tree configurations. Simulated annealing was used to search for an optimal configuration. Experimental results indicate that simulated annealing was very efficient on test cases that range from small to medium size networks, while ineffective on large networks. Although for large networks the nonconvexity of the cost function and the large solution space made searching for the optimal solution difficult, the accuracy (average success rate = 98.35%), and simplicity of our novel approach demonstrate its potential effectiveness in segmenting retinal vascular trees

Haemodynamics in the retinal vasculature during the progression of diabetic retinopathy

Introduction: Diabetic Retinopathy (DR) remains a major ocular disease, which can potentially lead to blindness if left untreated. The human retina is a very dynamic tissue, making it difficult to associate any changes with a disease and not with normal variability among people. 96 images from twenty-four subjects were used in this study, including the period of the three years before DR and the first year of DR (4 images per patient, one per year). Methods: The images were firstly segmented to obtain the vascular trees, selecting the same segments in the entire four-year period, to make a meaningful comparison. The trees, which included a parent vessel and two children branches, were connected using an implemented semi-automated tool. Some hemodynamic features were calculated, using the geometric measurements from the segmentation. At the branching points, the fluid dynamics conditions were estimated under the assumptions of Pouiseuille flow: stiff, straight and uniform tube. Blood fl ow velocity (v), blood fl ow rate (Q), Reynolds number (Re), pressure (P) and wall sheer stress (WSS) were calculated, both for arteries and veins. Blood viscosity (mu=0.04 P), tube ́s length (L) and diameter (D), were used to compute fl uid resistance to fl ow (R=128 mu L / pi D^4) through each vessel. Based on previous studies, the boundary conditions adopted to solve the problem were P_CRA = P_CRV = 45mmHg. Q_CRA and Q_CRV were derived from v_CRA, d_CRA, v_CRV, d_CRV by using the formula Q=VA. WSS was computed as WSS=32muQ/d^3. Re was calculated as Re=v d rho/mu, where rho=1.0515 g/mL is the blood density. Each feature (response variable) was analysed by using a linear mixed model, with the levels of the disease being the fixed effects explanatory variable, and the patients being the random effect with a random intercept. Results: Our study showed that veins were mostly affected during the last stages of the diabetic eye. Furthermore, the blood fl ow of both children and the Re in the small child branch were mostly affected in the arteries. Table 1 includes only the signifi cant features, with the relevant p-values (a=0.05) and Akaike Information Criterion (AIC). Conclusion: Alongside the already established importance of the retinal geometry, this study showed that the hemodynamic features can also be used as biomarkers of progression to DR. During this four-year period of the disease‘s progression, retina is adapting to the new underlying conditions

An adaptable deep learning system for optical character verification in retail food packaging

Retail food packages contain various types of information such as food name, ingredients list and use by dates. Such information is critical to ensure proper distribution of products to the market and eliminate health risks due to erroneous mislabelling. The latter is considerably detrimental to both consumers and suppliers alike. In this paper, an adaptable deep learning based system is proposed and tested across various possible scenarios: a) for the identification of blurry images and/or missing information from food packaging photos. These were captured during the validation process in supply chains; b) for deep neural network adaptation. This was achieved through a novel methodology that utilises facets of the same convolutional neural network architecture. Latent variables were extracted from different datasets and used as input into a k-means clustering and k-nearest neighbour classification algorithm, to compute a new set of centroids which better adapts to the target dataset’s distribution. Furthermore, visualisation and analysis of network adaptation provides insight into how higher accuracy was achieved when compared to the original deep neural network. The proposed system performed very well in the conducted experiments, showing that it can be deployed in real-world supply chains, for automating the verification process, cutting down costs and eliminating errors that could prove risky for public health

Deep Bayesian Self-Training

Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is often a challenge. In most real world problems, manual annotation is practically intractable due to time/labour constraints, thus the development of automated and adaptive data annotation systems is highly sought after. In this paper, we propose both a (i) Deep Bayesian Self-Training methodology for automatic data annotation, by leveraging predictive uncertainty estimates using variational inference and modern Neural Network architectures, as well as (ii) a practical adaptation procedure for handling high label variability between different dataset distributions through clustering of Neural Network latent variable representations. An experimental study on both public and private datasets is presented illustrating the superior performance of the proposed approach over standard Self-Training baselines, highlighting the importance of predictive uncertainty estimates in safety-critical domains

A deep learning approach to anomaly detection in nuclear reactors

In this work, a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and k-means clustering of representations. Monitoring nuclear reactors while running at nominal conditions is critical. Based on analysis of the core reactor neutron flux, it is possible to derive useful information for building fault/anomaly detection systems. By leveraging signal and image pre-processing techniques, the high and low energy spectra of the signals were appropriated into a compatible format for CNN training. Firstly, a CNN was employed to unfold the signal into either twelve or forty-eight perturbation location sources, followed by a k-means clustering and k-Nearest Neighbour coarse-to-fine procedure, which significantly increases the unfolding resolution. Secondly, a DAE was utilised to denoise and reconstruct power reactor signals at varying levels of noise and/or corruption. The reconstructed signals were evaluated w.r.t. their original counter parts, by way of normalised cross correlation and unfolding metrics. The results illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured/noisy signals, across various levels of granularity