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:  B. ANALYSIS PERFORMANCE OF EXISTING DEEP LEARNING METHODS FOR RETINAL BLOOD VESSEL EXTRACTION 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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.

Literature Review:
To give a clear picture of performance of each single method, we summarize their results in Table 2 that can show capability of detection of retinal blood vessels. We elaborate the strength and weakness of each method. Zhang et al. BIB001 validated their method only on DRIVE database, and their proposed method achieved 0.940.. As shown in Figure 11 , the tiny vessels are not detected properly. No validation was performed on the pathological images. Maji et al. BIB002 validated their method on DRIVE database, and their method shows good performance in terms of vessels detection. As shown in Figure 12 , tiny vessels are dropped. Also, there is no validation conducted on pathological images. Proper vessels length detection is not observed due to the unresolved issue of circle boundary around retinal vessels, and proper vessels were not analysed due to background noise pixels. Liskowski's BIB003 method achieved the detection accuracy of retinal vessels (shown in Figure 13 ) of 0.949 on STARE and DRIVE database. Again, the tiny vessels are not observed properly, and there is non-uniformity between blood vessels and background. Normalization technique could be applied to improve the proposed method. Fu et al. BIB004 improved their method based on CNN for retinal segmentation of blood vessels. As shown in Figure 14 , the tiny vessels are not detected, and the performance of their method is not validated on the challenging images (Images contains abnormalities or pathologies). Their method gives much better sensitivity of 0.760 on DRIVE database and 0.741 on STARE database along with an average accuracy of 0.95. Aaron Wu's method BIB005 was validated on DRIVE database only. Even though their method achieved 0.97 area under curve (AUC), there is no disclosure of the parameters in their experimental design. The output from their method is shown in Figure 15 , from which we can observe that the tiny vessels are completely dropped. Their method was not validated on the pathological images. An image enhancement technique can be applied on the images before training the CNN to improve the vessels detections results. Zhenjie Yao's method BIB006 gave much better sensitivity 0.773 on DRIVE database as compared to above methods but their method gave low accuracy of 0.936. No validation was performed on STARE dataset nor on the pathological images. The output of their method is shown in Figure 16 . As we observed, the tiny vessels are not detected even some large vessels were missing. Their method can be improved if image enhancement technique used as a pre-processing step. Maninis et al.  proposed a CNN model for retinal vessels segmentation and optic disc segmentation. The proposed method was validated on DRIVE and STARE databases but they reported only AUC of 0.822 on DRIVE database and 0.831 on STARE database. Figure 17 shows their method has the capability to segment the fine vessels but no validation was reported on pathological images. Their method can be one good tool for analysis retinal blood vessels after validation on challenging images. Huazhu Fu's BIB007 method was based on CNN for detection of tiny vessels. Their methods gave a good accuracy around average 95% on DRIVE and STARE database, but sensitivity is lower around 72% as compared to another methods. From the results of Fu's method shown in Figure 18 we can observe that tiny vessels are not detected properly. The performance of the method on the pathological images was not addressed. Their method can be improved by using the post-processing steps for removing noisy pixels to achieve fine vessels image. Tan et al. BIB008 validated their method on DRIVE database. The performance of their method was comparatively better FIGURE 17. Result of Kokitsi Maninis's method  . TFrom top to botton: row input images, ground truth images and segmented images.