You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Deep Learning Models for Retinal Blood Vessels Segmentation: A Review 
Chapter Title:  A. CONVOLUTIONAL NEURAL NETWORKS FOR RETINAL VESSELS SEGMENTATION 
1. Abstract of Cited Paper (BIB001):  Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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%. 
4. Abstract of Cited Paper (BIB004):  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.

Literature Review:
Basically, CNNs are a special type of artificial neural networks (ANNs) where the CNNs are designed to process data with grid-like structure. They receive a stack of input images with grid-like structure and output a group of feature maps that can be used for solving different tasks. To design a CNN model for segmenting the retinal vessels, the model should be able to generate the segmented image accurately and the available databases which will be used for training should be considered. Also, the loss function that will be used for training the model should be selected carefully. In classification problems for example, the CNN models consist of a consecutive convolutional layers followed by activation functions for the non-linearity and then pooling layers to decrease the feature maps size and pool the features. At the end of the models, fully-connected layers are used as task-specific layers to generate the desired output. In semantic segmentation and labeling, the fully-connected layers can be removed or replaced by convolutional layers (often with 1 × 1 kernels). Also, early-stage feature maps could help in semantic problems because they still contain the finer details of the scenes and objects in of the input image. The proposed model in BIB002 was perhaps the first to investigate the idea of merging feature maps with different levels of abstraction. In medical image segmentation, this trend led to the U-net  . With this idea, the encoder-decoder models became more popular for the medical image segmentation task. In the encoder-decoder model, the encoder network is a fully convolutional network where each convolutional layer is followed by an activation function. The decoder part is mirrored from the encoder, and it upsamples the feature maps generated from the encoder and try to segment the input image into different regions. For retinal vessels segmentation, the model should be able to classify each pixel as a vessel pixel or a background pixel. The output should be with the same resolution as the input image. The critical issue with the retinal vessels segmentation is to accurately detect and extract the tiny vessels. In the encoder-decoder model, skip connections are used to transfer the feature maps from the encoder to the decoder which help in the segmentation process and detect vessels with sharper edges. With respect to the loss functions, it is important to select a suitable loss function that can represent a useful meaning when comparing the output and the ground truth. The loss function is used to train the model and optimize the model weights. In the literature, different loss functions were used for retinal vessels segmentation. It depends on how the ground truth is presented. Looking to the ground truth images, the images are almost black (90% of the pixels are background), and the vessels' pixels form 10% of the whole image. If we train the network without considering this, the trained model will be biased towards the background. In the literature, BIB003 proposed to use softmax with log-likelihood as a loss function. They classify the pixels into 4 categories; the background, the optic disc, the fovea, and the blood vessels. In BIB005 , the output with two channels depth is generated from a softmax, where each pixel has two probability values that the maximum value indicates the class of the pixel (either a vessel or a background). Dice Loss is used as a loss function to optimize the model. Reference BIB004 uses the class-balancing cross entropy loss function BIB001 to train the proposed model and generate the segmented image. This loss function takes care of the heavily biased class distributions and balance the training process.