Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images

©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). “Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 186(105201), 105201. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2019.105201.[Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images. Method: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks. Results: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin. Conclusions: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions.This work is supported by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project, and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. The authors of this work also receive financial support from the ERDF and European Social Fund (ESF) of the EU, and Xunta de Galicia through Centro Singular de Investigación de Galicia, accreditation 2016–2019, ref. ED431G/01, Grupo de Referencia Competitiva, ref. ED431C 2016-047, and the predoctoral grant contract ref. ED481A-2017/328.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047Xunta de Galicia; ED481A-2017/32

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Color Fundus Image Registration Using a Learning-Based Domain-Specific Landmark Detection Methodology

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Medical imaging, and particularly retinal imaging, allows to accurately diagnose many eye pathologies as well as some systemic diseases such as hypertension or diabetes. Registering these images is crucial to correctly compare key structures, not only within patients, but also to contrast data with a model or among a population. Currently, this field is dominated by complex classical methods because the novel deep learning methods cannot compete yet in terms of results and commonly used methods are difficult to adapt to the retinal domain. In this work, we propose a novel method to register color fundus images based on previous works which employed classical approaches to detect domain-specific landmarks. Instead, we propose to use deep learning methods for the detection of these highly-specific domain-related landmarks. Our method uses a neural network to detect the bifurcations and crossovers of the retinal blood vessels, whose arrangement and location are unique to each eye and person. This proposal is the first deep learning feature-based registration method in fundus imaging. These keypoints are matched using a method based on RANSAC (Random Sample Consensus) without the requirement to calculate complex descriptors. Our method was tested using the public FIRE dataset, although the landmark detection network was trained using the DRIVE dataset. Our method provides accurate results, a registration score of 0.657 for the whole FIRE dataset (0.908 for category S, 0.293 for category P and 0.660 for category A). Therefore, our proposal can compete with complex classical methods and beat the deep learning methods in the state of the art.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00 136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095 894-B-I00 research project; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/147 and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). The funding institutions had no involvement in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED481A 2021/147Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

Retinal Blood Vessel Segmentation Algorithm for Diabetic Retinopathy using Wavelet: A Survey

Blood vessel structure in retinal images have an important role in diagnosis of diabetic retinopathy. There are several method present for automatic retinal vessel segmentation. For developing retinal screening systems blood vessel segmentation is the basic foundation since vessels serve as one of the main retinal landmark features. The most common signs of diabetic retinopathy include hemorrhages, cotton wool spots, dilated retinal veins, and hard exudates. A patient with diabetic retinopathy disease has to undergo periodic screening of eye. For the diagnosis, doctors use color retinal images of a patient required from digital fundus camera. We present a method that uses Gabor wavelet for vessel enhancement due to their ability to enhance directional structures and euclidean distance technique for accurate vessel segmentation. Retinal angiography images are mainly used in the diagnosis of diseases such as diabetic retinopathy and hypertension etc. In diabetic retinopathy structure of retinal blood vessels change that leads to adult blindness. To overcome this problem automatic biomedical diagnosis system is required.The main stage of diabetic retinopathy are Non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Eye care specialist can screen vessel abnormalities using an efficient and effective computer based approach to the automated segmentation of blood vessels in retinal images. Automated segmentation reduces the time required by a physician or a skilled technician for manual labeling. Thus a reliable method of vessel segmentation would be valuable for the early detection and characterization of changes due to such diseases. This article presents the automated vessel enhancement and segmentation technique for colored retinal images. Segmentation of blood vessels from image is a difficult task due to thin vessels and low contrast between vessel edges and background. The proposed method enhances the vascular pattern using Gabor wavelet and then it uses euclidean distance technique to generate gray level segmented image. DOI: 10.17762/ijritcc2321-8169.15030

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada