Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> VII. MAIN ISSUES IN RETINAL VESSELS METHODS <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> BIB001 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> VII. MAIN ISSUES IN RETINAL VESSELS METHODS <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> BIB002 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> VII. MAIN ISSUES IN RETINAL VESSELS METHODS <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> BIB003 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> VII. MAIN ISSUES IN RETINAL VESSELS METHODS <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> BIB004 </s> Deep Learning Models for Retinal Blood Vessels Segmentation: A Review <s> VII. MAIN ISSUES IN RETINAL VESSELS METHODS <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> BIB005
After analysing limitation and capabilities of each retinal vessels segmentation method based on deep learning, we identify VOLUME 7, 2019 that three main issues should be considered to implement a robust retinal blood vessels segmentation process. These issues are 1) In order to improve the sensitivity, the retinal blood vessels segmentation method based on deep learning technique should be capable to detect the tiny vessels. It means that the sensitivity of method should be improved. 2) The retinal blood vessel segmentation method should be capable to work on challenging images such as images contains abnormalities, central of light reflex and uneven illumination along with noise. The best way to analyze the performance of the method is to validate it on STARE database because it contains almost 50% challenging images. 3) Most segmented vessels images contain noise pixels in the background. Therefore, a robust design and trained CNN model or novel post-processing is required to handle the noisy pixels. The removal of the noisy pixels will give better visualization of retinal blood vessels, and it will be easier for the ophthalmologist to diagnose a disease and recommend proper treatment. We compared the previous methods according to the capability of solving these three core issues. Table 3 contains all CNN based retinal blood vessels segmentation methods. It represents the capabilities of these methods regarding detection of tiny vessels, working on pathological images and resolving the issue of noise. We rummage-sale check marks ( ) and X-mark (×) symbol. The shows that problem was successfully considered. The X-mark (×) symbol indicates that problem was not considered. We clearly observed that all methods solved the issue of noise, but they did not handle main issues of observation of tiny vessels and capability of working on pathologies. No method properly handles the issue of tiny vessels detection except some tiny vessels are detected by Toufique's method. It is this reason why we recommend proposing a novel contrast enhancement technique to enhance vessel network in order to detect tiny vessels properly in later stages. The methods such Fu et al. BIB001 , Liskowskie et al. BIB002 ,Toufique et al. BIB003 , Guo et al BIB004 and Yanet al BIB005 proposed models for segmentation of retinal vessels based on normalization techniques as pre-processing. This enable their methods to work on pathological images. The main reason of detection of tiny vessels in Toufique's method BIB003 is the use of post-processing step to remove the noise pixels from the background. However, there are a few tiny vessels that are still not detected.