Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researcher...Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks(CNNs), many tasks can be solved by CNNs.In this paper, we propose an encoding-decoding network with a pyramid self-attention module(PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks(FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE;B1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve(AUC) are 82.21%/80.57%,95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE;B1, respectively.展开更多
文摘Retina vessel segmentation is a vital step in diagnosing ophthalmologic diseases. Traditionally, ophthalmologists segment retina vessels by hand, which is time-consuming and error-prone. Thus, more and more researchers are committed to the research of automatic segmentation algorithms. With the development of convolution neural networks(CNNs), many tasks can be solved by CNNs.In this paper, we propose an encoding-decoding network with a pyramid self-attention module(PSAM) to segment retinal vessels. The network follows a U shape structure, and it comprises stacked feature selection blocks(FSB) and a PSAM. The proposed FSB consists of two convolution blocks with the same weight and a channel-wise attention block. At the head of the network, we apply a PSAM consisting of three parallel self-attention modules to capture long-range dependence of different scales. Due to the power of PSAM and FSB, the performance of the network improves. We have evaluated our model on two public datasets: DRIVE and CHASE;B1. The results show the performance of our model is better than other methods. The F1, Accuracy, and area under curve(AUC) are 82.21%/80.57%,95.65%/97.02%, and 98.16%/98.46% on DRIVE and CHASE;B1, respectively.
