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Diverter transformer-based multi-encoder-multi-decoder network model for medical retinal blood vessel image segmentation

计算机科学 编码器 分割 变压器 人工智能 计算机视觉 视网膜 图像分割 医学 眼科 电压 电气工程 工程类 操作系统
作者
Chengwei Wu,Min Guo,Miao Ma,Kaiguang Wang
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:93: 106132-106132 被引量:4
标识
DOI:10.1016/j.bspc.2024.106132
摘要

The retinal blood vessel is an essential part of the fundus structure. It is important to accurately analyze the structure and distribution of retinal vessels, which can help make accurate medical diagnoses. However, it is still challenging to extract detailed information due to the problems of fuzzy edges, low resolution, and lots of noise in retinal blood vessel medical images. To extract the image detail information effectively, we propose a new diverter transformer-based multi-encoder-multi-decoder network model in this paper. The network model consists of a feature encoder module and a feature decoder module. Among them, the feature encoding module consists of a diverter transformer with a diverter adaptive mechanism, three encoder units with a convolution layer and max-pooling layer, and the two decoder units in the feature decoding module consist of an inverse convolution layer and an up-sampling layer, respectively. The Local Context Module (LCNet Module) in the feature encoding module learns richer local context feature information layer by layer through changing the width of the network while downsampling; the Global Encoder Module1 (G-Encoder Module1) and the Global Encoder Module2 (G-Encoder Module2) extract the global feature representation of retinal blood vessel images by performing a max-pooling operation to transform the input data into a vector of fixed dimensions, thus helping the network model to better understand and extract the global feature representation of retinal blood vessel images. The two decoder units in the feature decoding module receive local and global feature information from three encoder units, LCNet Module, G-Encoder Module1 and G-Encoder Module2, respectively. Decoder Module1 generates segmentation prediction by layer-by-layer up-sampling operation, and Decoder Module2 recovers the feature information by downsampling and decoding operations and fuses the recovered feature information to output, obtaining the final segmentation of the retinal blood vessels. The proposed diverter transformer-based multi-encoder-multi-decoder network model is validated on the DRIVE and STARE datasets with other classical and state-of-the-art network models, and its segmentation accuracy is 97.25% and 97.93%, respectively. Compared with the classical U-Net model, the improvement is 2.24% and 1.42%, respectively. Compared with the state-of-the-art SPNet model, the accuracy is increased by 0.61% on DRIVE and 1.01% on STARE. It indicates that the network model proposed in this paper has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.
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