Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve. <s> BIB001 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Changes in retinal blood vessel features are precursors of serious diseases such as cardiovascular disease and stroke. Therefore, analysis of retinal vascular features can assist in detecting these changes and allow the patient to take action while the disease is still in its early stages. Automation of this process would help to reduce the cost associated with trained graders and remove the issue of inconsistency introduced by manual grading. Among different retinal analysis tasks, retinal blood vessel extraction plays an extremely important role as it is the first essential step before any measurement can be made. In this paper, we present an effective method for automatically extracting blood vessels from colour retinal images. The proposed method is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved. To maintain the strength and eliminate the drawbacks of each individual line detector, the line responses at varying scales are linearly combined to produce the final segmentation for each retinal image. The performance of the proposed method was evaluated both quantitatively and qualitatively on three publicly available DRIVE, STARE, and REVIEW datasets. On DRIVE and STARE datasets, the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) while retaining comparable accuracy compared to other existing methods. Visual inspection on the segmentation results shows that the proposed method produces accurate segmentation on central reflex vessels while keeping close vessels well separated. On REVIEW dataset, the vessel width measurements obtained using the segmentations produced by the proposed method are highly accurate and close to the measurements provided by the experts. This has demonstrated the high segmentation accuracy of the proposed method and its applicability for automatic vascular calibre measurement. Other advantages of the proposed method include its efficiency with fast segmentation time, its simplicity and scalability to deal with high resolution retinal images. <s> BIB002 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. <s> BIB003 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Background: Blood vessel tortuosity analysis in fundus image is used to develop a Clinical Decision Support System (CDSS) to diagnose hypertensive retinopathy, retinopathy of prematurity, cardiovascular problem and stroke. Methods: A Novel approach using machine learning algorithms has been proposed in this paper to determine global tortuosity in a clinical perspective. After preprocessing and blood vessel extraction, eight dimensional feature vector is formed by evaluating tortuosity. By applying Correlation based feature selection procedure top four features are selected for classification purpose. We have collected images from database and hospital, and images are graded by senior ophthalmologist under two labels namely normal and tortuous image. Findings: Highest sensitivity is obtained in the case of SVM classifier. By keeping clinical classification as ground truth highest sensitivity of 96.6% is achieved in case of SVM with radial basis function as kernel. Feature selection process improves the overall sensitivity by 4% and also reduces the computational complexity. In the proposed method decision making is done based on the four features followed by classification, which outperform the other methods in the literature. By using the novel feature-classifier combination, highest sensitivity is obtained. Improvement: In the proposed method decision making is done based on the four features, which outperform the other method proposed in the literature. By using the novel feature-classifier combination, highest sensitivity is obtained. <s> BIB004 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. <s> BIB005 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Diabetic Retinopathy is human eye disease which causes damage to retina of eye and it may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Many physical tests like visual acuity test, pupil dilation, optical coherence tomography can be used to detect diabetic retinopathy but are time consuming and affects patients as well. This review paper focuses on decision about the presence of disease by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image processing algorithms, like diameter of optic disk, lesion specific (microaneurysms, exudates), image level (pre-screening, AM/FM, quality assessment). Decision making for predicting the presence of diabetic retinopathy was performed using alternating decision tree, adaBoost, Naive Bayes, Random Forest and SVM. <s> BIB006 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance—on par with two human experts—was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set. <s> BIB007 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Eye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions. <s> BIB008 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI. <s> BIB009 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> This paper proposed an improved pixel-classification based method for artery and vein classification on retinal image.Intra-image regularization and inter-subject normalization are first used to reduce the image differences in feature space.Novel features, including first-order and second-order texture features, are introduced to capture the discriminating characteristics.A high accuracy of 0.923 was achieved on a public database.The proposed method holds great potential to serve as an early diagnostic tool for various diseases, such as diabetic retinopathy. (Background and objectives)Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. (Methods)Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. (Results)The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. (Conclusion)This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases. <s> BIB010 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> Abstract 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. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. <s> BIB011 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> This paper presents a novel two-stage image segmentation method using an edge scaled energy functional based on local and global information for intensity inhomogeneous image segmentation. In the first stage, we integrate global intensity term with a geodesic edge term, which produces a preliminary rough segmentation result. Thereafter, by taking final contour of the first stage as initial contour, we begin second stage segmentation process by integrating local intensity term with geodesic edge term to get final segmentation result. Due to the suitable initialization from the first stage, the second stage precisely achieves desirable segmentation result for inhomogeneous image segmentation. Two stage segmentation technique not only increases the accuracy but also eliminates the problem of initial contour existed in traditional local segmentation methods. The energy function of the proposed method uses both global and local terms incorporated with compacted geodesic edge term in an additive fashion which uses image gradient information to delineate obscured boundaries of objects inside an image. A Gaussian kernel is adapted for the regularization of the level set function and to avoid an expensive re-initialization. The experiments were carried out on synthetic and real images. Quantitative validations were performed on Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2015 and PH2 skin lesion database. The visual and quantitative comparisons will demonstrate the efficiency of the proposed method. <s> BIB012 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> I. INTRODUCTION <s> An automatic retinal vessel segmentation method is proposed for quick and accurate segmentation of eye vessels. Such a method would play a vital role in analyzing many eye diseases. A retinal fundus image contains varying low contrasts, which undermine the performance of the segmentation process. Independent component analysis (ICA) is largely used for noise removal and consists of two architectures, designated as ICA1 and ICA2. We have validated both ICA architectures on retinal color fundus images and selected the one that provides improved contrast values. For retinal fundus, ICA2 architecture performed better than ICA1 by virtue of being more effective in compensating the low contrast values. Experiments conducted here validated the improvements over previously reported state-of-the-art methods. The impact of proposed segmentation model was assessed on publicly available databases like DRIVE and STARE. In case of the DRIVE database, the sensitivity increased 3% by (from 72% to 75%) while maintaining a segmentation accuracy of around 96%. <s> BIB013
Eye diseases, such as diabetic retinopathy (DR) and Diabetic maculopathy (MD), are the main causes of global blindness. MD has a long preclinical phase where visual acuity is not affected and once the patient presents to the eye clinic the vision loss can often not be recovered. Early detection can prevent disease progression and protect vision. Therefore the study and the analysis of retinal vessel geometric characteristics such as vessel diameter, branch angles, and branch lengths have become the basis of medical applications related to early diagnosis and effective monitoring of retinal pathology. Retinal image assessment by ophthalmologists is a vital step for identification of retinal pathology. The associate editor coordinating the review of this manuscript and approving it for publication was Imran Sarwar Bajwa. The computerized image analysis has been successfully applied in various medical applications since 1982 BIB008 ,  . Aiming at increasing both efficiency and the accuracy, various automatic computerized medical image analysis approaches have been developed since then BIB001 . Image segmentation has been seen as a wide application at that stage before the 1990s. Recently, more artificial intelligence-based approaches such as supervised methods have become popular in this area. Pattern recognition and machine learning methods are very productive due to their success in medical image analysis system BIB008 , BIB009 . However, implementing a robust computerized method for medical image analysis is still a challenging task nowadays due to the complexity and heterogeneity of the retinal images BIB012 . Often it requires user interaction to diagnose the diseases by searching the most distinguished features from the images BIB002 . There has been a consistent research effort made in the area of retinal image processing. Some data mining approaches have targeted extracting the most efficient features. Low-level features such as morphological features, colour features, vessel width, texture, sharpness of vessel boundary as well as the coarseness inside a blood vessel have been incorporated, although some require advanced technology and are not suitable for remote clinics without trained specialists. Middle-level features such as a saliency map extracted from fundus images are applied to train classifiers with the purpose of extracting retinal blood vessels BIB010 . Others treat such tasks as solving an optimal function in terms of minimizing the energy cost or distances. Examples include graph-based approach, K-nearest neighbours (KNN), Gaussian mixture model and entropy thresholding based BIB004 , BIB013 model. Different machine learning based approaches have shown better performance in this area. Support Vector Machine (SVM), decision tree, AdaBoost, Naive Bayes and Random Forest BIB006 , BIB011 are examples of these and have been investigated by researchers. Moreover, ensemble of machine learning classifying algorithms were proposed to improve the final accuracy while combining the outputs of different retinal image processing algorithms BIB001 . Deep learning based approaches are playing an important role in the segmentation of digital images, and these approaches outperform existing methods based on image processing techniques. The main advantage of using deep learning is the suitability of training of large database for classification or segmentation which makes deep learning based approaches different and effective from other previous implemented methods. In 2006, a deep convolutional neural network (CNN) structure was designed for different imaging tasks of natural images  that intended to classify different features of colour images. AlexNet  , VGGNet BIB003 and the inception of the architecture of GoogLeNet BIB005 have been applied successfully to a large variety of objects identification and semantic segmentation. In the area of retinal image analysis, the CNN is used to detect retinal vessels and to classify patch features into different regions of vessels and in different connecting cases of the vessels. Several researchers have worked on CNN based models for detection of vessels but the accuracy diminishes as the blood vessels get smaller in diameter. Krizhevsky et al.  contributed to the watershed transformation of the image by using the ImageNet challenge in 2012, and they proposed a CNN model called AlexNet, winning the challenge with a significant margin. Due to the good performance of CNN model, the medical image analysis community too has considered implementation of CNN. In this paper, we review the deep learning based articles published within the recent five years in medical image analysis. More specifically, this paper focuses on papers on retinal color fundus images for segmentation as well as abnormality analysis in the retinal fundus image. Three main purposes of doing such a survey of deep learning methods on retinal image analysis include 1) Demonstrating that the deep learning based methods have influence in the retinal image analysis as compared to other techniques. 2) Highlighting the specific contribution which solves the accurate segmentation of retinal blood vessels problems. 3) Studying different proposed deep learning models on retinal images and observing their performance as well as the limitation, and proposing the suitable methodology to overcome such limitations in order to improve performance. We also list different types of medical image analysis applications using deep learning in Figure 1 . Figure 2 shows the number of papers that discuss different modalities such as MRI, CT, X-ray and fundus images. The first deep learning based paper on retinal fundus images was published (Kaggle Diabetic Retinopathy challenge 2015, image from van Grinsven et al. BIB007 ). There have been only 17 deep learning based papers published since then (as shown in Figure 2 ), 12 of which are related to retinal vessels segmentation and that is the focal area of our interest. Here, we present a detailed analysis of these 12 methods by investigating and comparing their advantages as well as VOLUME 7, 2019 limitations. Using this approach we identify the best method for fast and accurate retinal image analysis that will hopefully ease the issues faced by Australia's regional community of unmet demand for professional clinical services. The finding of this paper will assist the early diagnose of retinal diseases by providing an efficient and reliable approach based on the most recent machine learning analysis. This paper foresees the future direction of how the new technology will help limit vision loss, improving the early diagnosis accuracy for effective treatments and thereby significantly improve the life quality of patients with eye diseases. This paper explores and identifies a flexible and reliable computerized system that can automatically identify the change in the blood vessels and predict the potential disease as early as possible. The paper is organized as follows. Section II contains an overview of deep learning methods. Section III explains the CNN in details. Section IV contains detailed explanation of importance of segmentation by using CNN. Section V describes the details of measuring parameters and the used database in the existing methods. Section VI reviews the state of art of retinal vessels segmentation methods based on deep learning, and analyze the methodologies of each method. Section VII analyze the main issues in retinal vessels methods based deep learning. Section VIII outlines the concluding remarks and future research directions.