Analysis of vessel connectivities in retinal images by cortically inspired spectral clustering

\u3cp\u3eRetinal images provide early signs of diabetic retinopathy, glaucoma, and hypertension. These signs can be investigated based on microaneurysms or smaller vessels. The diagnostic biomarkers are the change of vessel widths and angles especially at junctions, which are investigated using the vessel segmentation or tracking. Vessel paths may also be interrupted; crossings and bifurcations may be disconnected. This paper addresses a novel contextual method based on the geometry of the primary visual cortex (V1) to study these difficulties. We have analyzed the specific problems at junctions with a connectivity kernel obtained as the fundamental solution of the Fokker–Planck equation, which is usually used to represent the geometrical structure of multi-orientation cortical connectivity. Using the spectral clustering on a large local affinity matrix constructed by both the connectivity kernel and the feature of intensity, the vessels are identified successfully in a hierarchical topology each representing an individual perceptual unit.\u3c/p\u3

Exploratory study on direct prediction of diabetes using deep residual networks

\u3cp\u3eDiabetes is threatening the health of many people in the world. People may be diagnosed with diabetes only when symptoms or complications such as diabetic retinopathy start to appear. Retinal images reflect the health of the circulatory system and they are considered as a cheap and patient-friendly source of information for diagnosis purposes. Convolutional neural networks have enhanced the performance of conventional image processing techniques significantly by neglecting inconsistent feature extraction pipelines and learning informative features automatically from data. In this work we explore the possibility of using the deep residual networks as one of the state-of-the-art convolutional networks to diagnose diabetes directly from retinal images, without using any blood glucose information. The results indicate that convolutional networks are able to capture informative differences between healthy and diabetic patients and it is possible to differentiate between these two groups using only the retinal images. The performance of the proposed method is significantly higher than human experts.\u3c/p\u3

Risk of Training Diagnostic Algorithms on Data with Demographic Bias

One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algorithms trained with biased datasets can easily lead to erroneous or discriminatory conclusions. This is even more crucial in clinical applications where the predictive algorithms are designed mainly based on a limited or given set of medical images and demographic variables such as age, sex and race are not taken into account. In this work, we conduct a survey of the MICCAI 2018 proceedings to investigate the common practice in medical image analysis applications. Surprisingly, we found that papers focusing on diagnosis rarely describe the demographics of the datasets used, and the diagnosis is purely based on images. In order to highlight the importance of considering the demographics in diagnosis tasks, we used a publicly available dataset of skin lesions. We then demonstrate that a classifier with an overall area under the curve (AUC) of 0.83 has variable performance between 0.76 and 0.91 on subgroups based on age and sex, even though the training set was relatively balanced. Moreover, we show that it is possible to learn unbiased features by explicitly using demographic variables in an adversarial training setup, which leads to balanced scores per subgroups. Finally, we discuss the implications of these results and provide recommendations for further research

Curvature integration in a 5D kernel for extracting vessel connections in retinal images

Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems for large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. In this paper, we propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a five-dimensional kernel obtained as the fundamental solution of the Fokker-Planck equation modelling the cortical connectivity in the lifted space of positions, orientations, curvatures and intensity. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy and challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques

Retrieving challenging vessel connections in retinal images by line co-occurrence statistics

Natural images contain often curvilinear structures, which might be disconnected, or partly occluded. Recovering the missing connection of disconnected structures is an open issue and needs appropriate geometric reasoning. We propose to find line co-occurrence statistics from the centerlines of blood vessels in retinal images and show its remarkable similarity to a well-known probabilistic model for the connectivity pattern in the primary visual cortex. Furthermore, the probabilistic model is trained from the data via statistics and used for automated grouping of interrupted vessels in a spectral clustering based approach. Several challenging image patches are investigated around junction points, where successful results indicate the perfect match of the trained model to the profiles of blood vessels in retinal images. Also, comparisons among several statistical models obtained from different datasets reveal their high similarity, i.e., they are independent of the dataset. On top of that the best approximation of the statistical model with the symmetrized extension of the probabilistic model on the projective line bundle is found with a least square error smaller than 2%. Apparently, the direction process on the projective line bundle is a good continuation model for vessels in retinal images

Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images

We propose a novel Brain-Inspired Multi-Scales and Multi-Orientations (BIMSO) segmentation technique for the retinal images taken with laser ophthalmoscope (SLO) imaging cameras. Conventional retinal segmentation methods have been designed mainly for color RGB images and they often fail in segmenting the SLO images because of the presence of noise in these images. We suppress the noise and enhance the blood vessels by lifting the 2D image to a joint space of positions and orientations (SE(2)) using the directional anisotropic wavelets. Then a neural network classifier is trained and tested using several features including the intensity of pixels, filter response to the wavelet and multi-scale left-invariant Gaussian derivatives jet in SE(2). BIMSO is robust against noise, non-uniform luminosity and contrast variability. In addition to preserving the connections, it has higher sensitivity and detects the small vessels better compared to state-of-the-art methods for both RGB and SLO images

Optimization of the TiN and TiZrN films arrangement process in stainless steel using factorial experimental design.

Filmes finos de Nitreto de titânio (TiN) e Nitreto de titânio-zircônio (TiZrN) foram depositados sobre substratos de aço inoxidável 316 usando o método de Sputtering RF para deposição dos filmes. O planejamento de experimentos (DOE) tem sido reconhecido como um método poderoso para otimizar um processo complexo na indústria. Os efeitos do presente estudo foram verificar a viabilidade e confiabilidade da aplicação do método DOE em processos de Sputtering RF, otimizar os parâmetros de processamento para o processo de deposição, identificando os parâmetros sensíveis que afetam a espessura da camada depositada (E.C.D) e a resistência à corrosão (Ecorr.). Para o método de Sputtering RF, dois parâmetros, a taxa e tempo de deposição foram escolhidos para serem os parâmetros do processo. Depois da deposição, a estrutura de camada depositada foi caracterizada por Difração de Raios X (DRX) e por Microscopia Eletrônica de Varredura (MEV). Após o ensaio de polarização, a corrosão foi realizada a fim de investigar a relação entre o início da corrosão e a espessura da camada depositada. A análise de variância (ANOVA) foi realizada para avaliar os parâmetros sensíveis e prever as condições ideais. Com base na análise estatística, os parâmetros mais sensíveis no processo de Sputtering RF foram tanto a taxa como o tempo de deposição do filme fino. As melhores condições de deposição foram a taxa de deposição máxima e tempo máximo.Titanium nitride (TiN) and titanium-zirconium nitride (TiZrN) thin films were deposited on ASTM F 138 stainless steel substrates using de Sputtering RF methods. Design of experiment (DOE) has long been recognized as a powerful method to optimize a complex process in industry. The purposes of present study were to verify the feasibility and reliability of the application of DOE method on de Sputtering RF processes and optimize the processing parameters for the deposition process, in which the sensitive parameters that affected the film properties were also identified. For de Sputtering RF method, two parameters, deposition rate and time were chosen to be the operating parameters. After deposition, the thin film structure was characterized by X-ray diffraction (XRD), and high-resolution scanning electron microscopy (SEM). After the polarization test, the corrosion analysis was carried out in order to investigate the relationship between the corrosion initiation and the thickness of the deposited layer. The analysis of variance (ANOVA) was conducted to assess the sensitive parameters and predict the optimum conditions. Based on the statistical analysis, the most sensitive parameters in de Sputtering RF process were both the deposition rate and time. The optimum deposition conditions in each system were maximum deposition rate and time

Retinal health information and notification system (RHINO)

\u3cp\u3eThe retinal vasculature is the only part of the blood circulation system that can be observed non-invasively using fundus cameras. Changes in the dynamic properties of retinal blood vessels are associated with many systemic and vascular diseases, such as hypertension, coronary heart disease and diabetes. The assessment of the characteristics of the retinal vascular network provides important information for an early diagnosis and prognosis of many systemic and vascular diseases. The manual analysis of the retinal vessels and measurement of quantitative biomarkers in large-scale screening programs is a tedious task, time-consuming and costly. This paper describes a reliable, automated, and efficient retinal health information and notification system (acronym RHINO) which can extract a wealth of geometric biomarkers in large volumes of fundus images. The fully automated software presented in this paper includes vessel enhancement and segmentation, artery/vein classification, optic disc, fovea, and vessel junction detection, and bifurcation/crossing discrimination. Pipelining these tools allows the assessment of several quantitative vascular biomarkers: width, curvature, bifurcation geometry features and fractal dimension. The brain-inspired algorithms outperform most of the state-of-the-art techniques. Moreover, several annotation tools are implemented in RHINO for the manual labeling of arteries and veins, marking optic disc and fovea, and delineating vessel centerlines. The validation phase is ongoing and the software is currently being used for the analysis of retinal images from the Maastricht study (the Netherlands) which includes over 10,000 subjects (healthy and diabetic) with a broad spectrum of clinical measurements.\u3c/p\u3

Robust and fast vessel segmentation via Gaussian derivatives in orientation scores

\u3cp\u3eWe propose a robust and fully automaticmatched filter-based method for retinal vessel segmentation. Different from conventional filters in 2D image domains, we construct a new matched filter based on secondorder Gaussian derivatives in so-called orientation scores, functions on the coupled space of position and orientations ℝ2 ⋊ S\u3csup\u3e1\u3c/sup\u3e. We lift 2D images to 3D orientation scores by means of a wavelet-type transform using an anisotropic wavelet. In the domain ℝ2 ⋊ S\u3csup\u3e1\u3c/sup\u3e, we set up rotation and translation invariant second-order Gaussian derivatives. By locally matching the multi-scale second order Gaussian derivative filters with data in orientation scores, we are able to enhance vessel-like structures located in different orientation planes accordingly. Both crossings and tiny vessels are well-preserved due to the proposed multi-scale and multi-orientation filtering method. The proposed method is validated on public databases DRIVE and STARE, and we show that the method is both fast and reliable. With respectively a sensitivity and specificity of 0.7744 and 0.9708 on DRIVE, and 0.7940 and 0.9707 on STARE, our method gives improved performance compared to state-of-the-art algorithms.\u3c/p\u3