Retinal vessel segmentation based on task‐driven generative adversarial network

Abstract

Retinal vessel segmentation has important application value in clinical diagnosis. If experts manually segment the retinal vessels, the workload is heavy, and the result is strong subjectively. However, some existing automatic segmentation methods have the problems of incomplete vessel segmentation and low‐segmentation accuracy. In order to solve the above problems, this study proposes a retinal vessel segmentation method based on task‐driven generative adversarial network (GAN). In the generative model, a U‐Net network is used to segment the retinal vessels. In the discriminative model, multi‐scale discriminators with different receptive fields are used to guide the generative model to generate more details. On the other hand, in view of the uncontrollable characteristics of the data generated by the traditional GAN, a task‐driven model based on perceptual loss is added to traditional GAN for feature matching, which makes the generated image more task‐specific. Experimental results show that the accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of the proposed method on data set digital retinal images for vessel extraction are 96.83, 80.66, 98.97 and 0.9830%, respectively.

Segmentation for Athlete's Ankle Injury Image Using Residual Double Attention U-Net Model

“…The comparison results of DSC values of different algorithms are shown in Figure 5. According to the data in Figure 5, the maximum DSC value of the proposed algorithm is 0.93, which is 0.01, 0.13, 0.13, 0.1 and 0.06 higher than the algorithms in MDAN [3], RVSTGAN [4], DNAS [5], TANN [6] and LEAC [7], respectively; The minimum DSC value of the proposed algorithm is 0.93, which is 0.14, 0.19, 0.18, 0.19 and 0.18 higher than the algorithms in MDAN [3], RVSTGAN [4], DNAS [5], TANN [6] and LEAC [7], respectively. It shows that the DSC value of the proposed algorithm is higher than the algorithm in MDAN [3], the algorithm in RVSTGAN [4], the algorithm in DNAS [5], the algorithm in TANN [6] and the algorithm in LEAC [7], which indicates that the actual segmentation effect of the proposed algorithm is higher than the theoretical segmentation effect, and the actual application effect is better.…”

“…The algorithm in MDAN [3], the algorithm in RVSTGAN [4], the algorithm in DNAS [5], the algorithm in TANN [6] and the algorithm in LEAC [7] as well as the algorithm experimental comparison method in this paper are compared, and five performance evaluation indexes are selected to verify the segmentation performance of different methods.…”

“…After testing, it was found that the value of the algorithm is relatively low, indicating that there is a problem of missegmentation in the image segmentation process of ankle joint injury of athletes using the algorithm, and the actual application effect is not good. Chen Z and coauthors [4] used task-driven generation of confrontation networks to realize image segmentation of retinal blood vessels. In the generation model, the U network was used to segment retinal blood vessels.…”

IterNet++: An improved model for retinal image segmentation by curvelet enhancing, guided filtering, offline hard‐sample mining, and test‐time augmenting

“…In recent years, deep learning models [23][24][25][26] have become popular among researchers because of higher performance achieved in medical image segmentation. These models are often based on neural networks and supervised segmentation, including FCN-based segmentation models, U-Net-based segmentation models, and deep learning models with auxiliary information, which are briefly described below.…”

“…However, methods based on FCNs often failed to achieve satisfy results in medical image segmentation on small size dataset and specialised domains. Therefore, U-Net [8] and its variants [24,25] were more popular among researchers. They exploited multiple layers of decoders instead of a single fully connected layer for segmentation through an encoderdecoder architecture.…”