Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Blood vessel segmentation of retinal images plays an important role in the diagnosis of eye diseases. In this paper, we propose an automatic unsupervised blood vessel segmentation method for retinal images. Firstly, a multi-dimensional feature vector is constructed with the green channel intensity and the vessel enhanced intensity feature by the morphological operation. Secondly, self-organizing map (SOM) is exploited for pixel clustering, which is an unsupervised neural network. Finally, we classify each neuron in the output layer of SOM as retinal neuron or non-vessel neuron with Otsu’s method, and get the final segmentation result. Our proposed method is validated on the publicly available DRIVE database, and compared with the state-of-the-art algorithms. <s> BIB001 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> We develop a new edge detection algorithm that addresses two critical issues in this long-standing vision problem: (1) holistic image training, and (2) multi-scale feature learning. Our proposed method, holistically-nested edge detection (HED), turns pixel-wise edge classification into image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are crucially important in order to approach the human ability to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of 0.782) and the NYU Depth dataset (ODS F-score of 0.746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than recent CNN-based edge detection algorithms. <s> BIB002 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the individual ConvNets of the ensemble are averaged to form the final segmentation. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with maximum average accuracy of 94.7\% and area under ROC curve of 0.9283. <s> BIB003 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400 $\thinspace$ 000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to $ > {0.99}$ ) and accuracy of classification (up to $ > {0.97}$ ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( ${\rm sensitivity} > {0.87}$ ), and fares well on pathological cases. <s> BIB004 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets. <s> BIB005 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Analysis of vascular geometry is important in many medical imaging applications, such as retinal, pulmonary, and cardiac investigations. In order to make reliable judgments for clinical usage, accurate and robust segmentation methods are needed. Due to the high complexity of biological vasculature trees, manual identification is often too time-consuming and tedious to be used in practice. To design an automated and computerized method, a major challenge is that the appearance of vasculatures in medical images has great variance across modalities and subjects. Therefore, most existing approaches are specially designed for a particular task, lacking the flexibility to be adapted to other circumstances. In this paper, we present a generic approach for vascular structure identification from medical images, which can be used for multiple purposes robustly. The proposed method uses the state-of-the-art deep convolutional neural network (CNN) to learn the appearance features of the target. A Principal Component Analysis (PCA)-based nearest neighbor search is then utilized to estimate the local structure distribution, which is further incorporated within the generalized probabilistic tracking framework to extract the entire connected tree. Qualitative and quantitative results over retinal fundus data demonstrate that the proposed framework achieves comparable accuracy as compared with state-of-the-art methods, while efficiently producing more information regarding the candidate tree structure. <s> BIB006 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> This paper proposes a CNN (Convolutional neural network) based blood vessel segmentation algorithm. Each pixel with its neighbors of the fundus image is checked by the CNN. The preliminary segmentation results of fundus images were refined by a two stages binarization and a morphological operation successively. The algorithm was tested on DRIVE dataset. While the specificity is 0.9603, sensitivity is 0.7731, which is very close to that of manual annotation. The sensitivity is 2% better than the ones found in current studies. The CNN based algorithm improves the segmentation of blood vessels performance significantly. <s> BIB007 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time. <s> BIB008 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Abstract We present a novel method to segment retinal images using ensemble learning based convolutional neural network (CNN) architectures. An entropy sampling technique is used to select informative points thus reducing computational complexity while performing superior to uniform sampling. The sampled points are used to design a novel learning framework for convolutional filters based on boosting. Filters are learned in several layers with the output of previous layers serving as the input to the next layer. A softmax logistic classifier is subsequently trained on the output of all learned filters and applied on test images. The output of the classifier is subject to an unsupervised graph cut algorithm followed by a convex hull transformation to obtain the final segmentation. Our proposed algorithm for optic cup and disc segmentation outperforms existing methods on the public DRISHTI-GS data set on several metrics. <s> BIB009 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalized before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the point’s neighbourhood and forwarded the response across the 7-layer network. The output layer consists of four neurons, representing background, optic disc, fovea and blood vessels. In average, our segmentation correctly classified 92.68% of the ground truths (on the testing set from Drive database). The highest accuracy achieved on a single image was 94.54%, the lowest 88.85%. A single convolutional neural network can be used not just to segment blood vessels, but also optic disc and fovea with good accuracy. <s> BIB010 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation.The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%. <s> BIB011 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Fundus images are one of the main methods for diagnosing eye diseases in modern medicine. The vascular segmentation of fundus images is an essential step in quantitative disease analysis. Based on the previous studies, we found that the category imbalance is one of the main reasons that restrict the improvement of segmentation accuracy. This paper presents a new method for supervised retinal vessel segmentation that can effectively solve the above problems. In recent years, it is a popular method that using deep learning to solve retinal vessel segmentation. We have improved the loss function for deep learning in order to better handle category imbalances. By using a multi-scale convolutional neural network structure and label processing approach, our results have reached the most advanced level. Our approach is a meaningful attempt to improve blood vessel segmentation and further improve the diagnostic level of eye diseases. <s> BIB012 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Abstract Background and objective The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results. Methods In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images. Results The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments. Conclusions BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at: https://github.com/guomugong/BTS-DSN . <s> BIB013 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> This paper presents a novel two-stage vessel segmentation framework applied to retinal fundus images. In the first stage a convolutional neural network (CNN) is used to correlate an image patch with a corresponding groundtruth reduced using Totally Random Trees Embedding. In the second stage training patches are forward propagated through CNN to create a visual codebook. The codebook is used to build a generative nearest neighbour search space that can be queried by feature vectors created through forward propagating previously-unseen patches through CNN. The proposed framework is able to generate segmentation patches that were not seen during training. Evaluated using publicly available datasets (DRIVE, STARE) demonstrated better performance than state-of-the-art methods in terms of multiple evaluation metrics. The accuracy, robustness, speed and simplicity of the proposed framework demonstrates its suitability for automated vessel segmentation. <s> BIB014 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Retinal vessel information is helpful in retinal disease screening and diagnosis. Retinal vessel segmentation provides useful information about vessels and can be used by physicians during intraocular surgery and retinal diagnostic operations. Convolutional neural networks (CNNs) are powerful tools for classification and segmentation of medical images. Complexity of CNNs makes it difficult to implement them in portable devices such as binocular indirect ophthalmoscopes. In this paper a simplification approach is proposed for CNNs based on combination of quantization and pruning. Fully connected layers are quantized and convolutional layers are pruned to have a simple and efficient network structure. Experiments on images of the STARE dataset show that our simplified network is able to segment retinal vessels with acceptable accuracy and low complexity. <s> BIB015 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Accurate segmentation of vessels is an arduous task in the analysis of medical images, particularly the extraction of vessels from colored retinal fundus images. Many image processing tactics have been implemented for accurate detection of vessels, but many vessels have been dropped. In this paper, we propose a deep learning method based on the convolutional neural network (CNN) with dice loss function for retinal vessel segmentation. To our knowledge, we are the first to form the CNN on the basis of the dice loss function for the extraction of vessels from a colored retinal image. The pre-processing steps are used to eliminate uneven illumination to make the training process more efficient. We implement the CNN model based on a variational auto-encoder (VAE), which is a modified version of U-Net. Our main contribution to the implementation of CNN is to replace all pooling layers with progressive convolution and deeper layers. It takes the retinal image as input and generates the image of segmented output vessels with the same resolution as the input image. The proposed segmentation method showed better performance than the existing methods on the most used databases, namely: DRIVE and STARE. In addition, it gives a sensitivity of 0.739 on the DRIVE database with an accuracy of 0.948 and a sensitivity of 0.748 on the STARE database with an accuracy of 0.947. <s> BIB016 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> A. ANALYSIS METHODOLOGY OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION <s> Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixel-wise loss that treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixel-wise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation, and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying nonvessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE, and CHASE_DB1 clearly demonstrate that the proposed three-stage deep learning model outperforms the current state-of-the-art vessel segmentation methods. <s> BIB017
Zhang et al. BIB001 was one of the first researchers who worked on detection of retinal blood vessels based on neural network techniques. They used some self organizing map (SOM) as pre-processing techniques to train the network to get retinal blood vessels, and their proposed unsupervised method gave a good retinal vessels segmentation. Their method consists of three steps. First, a multidimensional feature vector from the green channel of the RGB retinal image intensity is constructed, and then vessels enhanced intensities feature is achieved by using morphological operation. Next, a selforganizing map (SOM) with the concept of pixel clustering is used as a classifier, which is a type of unsupervised neural network. Finally, each neuron of the output layer of SOM is classified as retinal vessel neuron or retinal non-vessel neuron by applying Otsu's threshold method to achieve final vessel segmentation. Zillya et al. BIB009 proposed a method to identify of retinal blood vessels by using ensemble learning late fusion on convolutional neural network (CNN). Their method is based on the following three steps, 1) An entropy sampling method is used to select the informative points to reduce the computational complexity for performing the uniform sampling. 2) A CNN model based on convolutional filters was used. 3) A softmax logistic classifier is used to fuse the output of all learned filters, and to test the trained model on DRIVE database. Their observation was based on the graph cut algorithm because the output of the classifier is subjected to an unsupervised graph followed by a convex hull transformation to achieve the final segmentation of retinal vessels. Maji et al. BIB003 proposed a method by using ConvNetensemble based CNN architecture for processing the fundus color image in order to get retinal blood vessel. They used ensemble learning based on multiple models which seek to promote diversity among models with a combination, and it helps to reduce the problem of overfitting of training data. The main contribution of their method was an integration of ensemble learning with ConvNet to improve the generalization and this approach was a heuristics independent. In general, it provides a useful solution to solve complex medical data like retinal images. Tan et al. BIB010 proposed a seven-layer convolutional neural network (CNN) method. Their method not only detects the retinal blood vessels but also the optic disc and fovea of the retinal image. Due to detection of more than one feature at the same time, the accuracy of segmenting the retinal blood vessels was negatively affected. They used the normalization technique before segmentation to remove non-uniform background and noise in retinal images in such way it makes consistency in background lighting and contrast. The contribution of their work contains the selection of every effective vessels pixel in fundus image of three color channels before feeding into following CNN. Output layer contains four neurons which represents background, optic disc, fovea and blood vessels. Their method did not show satisfactory performance but it is the first method can do multi-task at the same time with a single CNN. Liskowski and Krawiec BIB004 proposed an unsupervised deep neural network (DNN) for analyzing retinal blood vessels. They used pre-processing steps such as global contrast normalization, zero-phase whitening, and augmentation using geometric transformations and gamma corrections for enhancing the intensities of blood vessels against their background. They used various classifiers such as Bayesian classifier and a nearest-neighbor classifier to classify the retinal image features. The features were learned with different ways to label vessels and non-vessels pixels for achieving vessels segmented image. Their method was performed much better than other existing methods. They suggested that learned features can be more efficient if using the other strong classifier in terms of detecting more vessel pixels correctly. However, retinal images suffer from background noise, uneven illumination and varying low contrast. The varying low contrast of retinal fundus images introduces difficulty to detect the vessels. Image enhancement technique can be included rather than improving variety of the features. Their approach did not show a good performance in case of tiny vessels detection. Fu et al. BIB005 developed a novel method for detecting retinal blood vessels based on probability map using CNN. They formulated the vessel extraction with consideration of boundary detection problem. The CNN is used to generate retinal vessels probability map. The vessel probability map differentiates vessels and background pixels in low contrast region. Their method performs better in the pathological images due to robustness of differentiating vessels and background pixels in low contrast region. Afterwards, they utilized fully-connected Conditional Random Fields (CRFs) with combination of the discriminative vessel probability map and long-range interactions between pixels to achieve 95% accuracy of segmentation. Wu et al. BIB006 developed a method for retinal blood vessels extraction by using CNN. They used PCA based nearest neighbor to local vessel pixels structure distribution. Afterwards, a generalized probabilistic tracking framework was used to segment retinal vessels. The main step of their method is to perform CNN for all image pixels either vessels pixels or background pixels. It loses the efficiency in final results. The combination of vessels tracking based on CNN and their probability distribution of vessels intensities improves the efficiency of their method, and provides more information of pixels of retinal blood vessels network. Their proposed method improves the efficiency of full image CNN results but loses minimal unconnected vessels pixels. This method can be improved if fewer pixels are segmented in the tracking approach as compared to the full CNN method. Yao et al. BIB007 proposed a method based on the CNN and post-processing steps to get the retinal vessels segmentation. The CNN checks each pixel with its neighbors of the retinal fundus image for making better contrast of retinal vessels against their background. Segmentation of retinal fundus image is achieved by using binarization as post-processing. The binarization contains two steps: First, they used local multi-scale and global normalization imaging technique to achieve the initial binarization results. They named this step as local binarization. Secondly, they used the morphological operation to improve segmented vessels image. The multiscale binarization is a good idea to get a well segment image but their proposed CNN model needs to improve to learn better-enhanced feature image. Their method can be improved if they use multiple scales with multiple CNN and ensemble each output. On the other hand, they should consider the morphological characteristics of retinal blood vessels as prior information during their post-processing step. In BIB008 , a deep CNN model combined with conditional random field (CRF) was proposed for retinal vessels segmentation. It consists of the following components, 1) They consider the retinal blood vessel segmentation issue as a boundary detection task. 2) The CNN is designed to learn a multi-scale discriminative representation with side output layers. It acts as a classifier that produces a companion local output for early layers BIB002 . 3) Conditional Random Field (CRF) is used to model for long-range interaction between the pixels. 4) CNN and CRF layers are combined into an integrated deep network and named as Deep Vessel output. Mahapatra et al.  implemented a method for retinal vessels detection which is based on the local saliency map and CNN model to get the vessels image. They used the unsupervised information from local saliency maps and the supervised information from the trained convolutional neural network. The final result is achieved by a combination of both saliency maps from unsupervised information and they train CNN from supervised information. The novelty of their method is the calculation of saliency values for every image pixel at multiple scales to provide the global and local image information. Such vessel pixel information provides an additional information for CNN. The combination of two-information improves the performance of the method as compared to other methods. The main purpose of their method is an assessment of image quality in order to analyze the retinal vessels, and the low computation of their method makes it possible for quick assessment of image quality and recommends the patient for timely treatment. Maninis et al.  proposed a framework for detection of retinal blood vessels and optic disc segmentation. The method is based on deep Convolutional Neural Networks (CNN), and they named their approach as Deep Retinal Image Understanding (DRIU). Their CNN architecture is based on the VGG network which is mostly used for image classification. In their method, the fully connected layers at the end of the network are removed. Four max-pooling layers are used between the convolutional layers, which separate the architecture into five stages. Between the pooling layers, feature maps are created by convolutions with different filters of the same size. The deeper network gives more information of features. They achieved two different feature maps for segmentation of retinal vessels and optic disc because they observed that the coarse feature maps at the last stage of CNN did not help with vessel detection, and thin vessels are not detected properly due to the fact that they down scaled the image layers by layers. The final vessel image of their method is based on probability map in which pixels detected as vessel or optic disc, and the method gives a good performance. However, their method does not show better results with images of optic disc shadow. Soomro et al. BIB011 proposed a method based on convolution neural network along with pre-and post-processing steps. They observed the three main issues impacting the final segmentation without preprocessing images. These issues are uneven illumination, noise, and low varying contrast. They select grey well-contrast image by using RGB into grey conversion. Many researchers just process green band image of retinal fundus image for vessels segmentation,else red and blue band also well-contrast. They use RGB to grey conversion to give better contrast images than green band images. First, they include pre-processing like morphological and PCA techniques. In the second step, the authors proposed a fully Convolutional Neural Network (CNN) and train it to get fine vessels features. But when there are noise pixels, tiny vessels are not detected properly. To address this, they add a post-processing step for getting well binarization images that removes noisy pixels. The output of their method is much better as compared to all other existing methods. Li et al. BIB012 proposed the method of analyzing fundus images by segmentation of retinal vessels, based on supervised vessel segmentation by the deep learning method. They found the problem of the imbalance of the retinal vessels which limits the improvement of the accuracy of VOLUME 7, 2019 the segmentation. They used the multi-scale CNN-based deep learning model with a modified loss function, and they called it the focal loss function. The improved loss function for deep learning to better handle the imbalance problem of vessel pixel segmentation. They achieved better results due to the use of the CNN multi-scale structure and the label processing method. Guo et al. BIB013 had proposed a method of vessel segmentation as well as pre-processing techniques to improve low contrast. They proposed CNN multi-scale and multi-level with deeply supervised CNN and short connection for the extraction of retinal vessels. They used short connections to transfer the semantic information of the vessels between the side-output layers of the network. There are two types of short connections: forward short connections and backward short connections. A forward short connection could transmit low-level semantic information at a high level, and backward short connection could transmit retinal vessel network information at a low level, resulting in a well-segmented image of the vessels. For further validation, they use a structural similarity measure to evaluate the segmented image of the vessels. Chudzik et al. BIB014 proposed the method based on two-stage vessel segmentation from retinal fundus images. In the first stage, they used CNN to correlate an image patch with a corresponding groundtruth; they used totally random trees embedding for this task. In a second step, training patches are given to CNN for the creation of the visual codebook. This codebook was used to form the nearest neighbour vectors created by the propagation of CNN's invisible patches. The main contribution of their method to the generation of segmentation patches that were not detected during the training process. The performance of the proposed framework demonstrated the validation of their method for segmentation of retinal blood vessels. Hajabdollahi et al. BIB015 proposed the CNN method based on a combination of CNN quantization and pruning for retinal vessel segmentation. They used the quantized technique on fully connected layers and a pruning tactic on the convolutional layers to form a very efficient and sample network structure. Their method worked well on the most used database and gave better results with reduced complexity. Yan et al. BIB017 proposed both imbalances the detection of thin and thick retinal vessels. Because thick and thin vessels are important for proper detection. Numerous deep learning methods as well as image-based retinal vein detection method attempt to simultaneously segment thin and thick vessels using pixel-level unit loss that processes all pixels (vessels. or non-vessel pixels) with the same consideration. But there is a high imbalance ratio between thick and thin vessels, and most thick vessels dominate thin vessels. This is one of the reasons that most segmented retinal methods have not used thin vessels, which leads to poor performance. They proposed the method for separately segmenting thick and thin vessels, and their method is based on three-steps in-depth learning tactics. The first step was based on the detection of thick vessels and the second step on the detection of thin vessels, and the last step was based on the stage of fusion to refine the results by recognizing other thin and thick vessels and to give the image of the final well-segmented vessel. Soomro et al. BIB016 proposed the method based on the CNN model with the dice loss function. The first time dice loss function is used for segmentation of retinal vessels. The method involved two steps. The first step was based on the pre-processing steps to eliminate uneven illumination. The main purpose of preprocessing steps is to make the training process more efficient. The second step was based on the CNN model that contained a variational auto-encoder that is a modified version of U-Net. The main contribution of their method to the implementation of the CNN model is to replace all layers of pooling by progressive convolution and deeper layers. It maintained the resolution and gave exactly the same resolution segmented image as the original image resolution. The proposed method gave better performances and made it possible to overcome the problem of imbalance related to the segmentation of the retinal vessels.