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- W4382246144 abstract "Automatic segmentation of retinal blood vessels is a nontrivial task due to the complexity of retinal fundus image. In this paper, a new network named MFCTrans-net is proposed for retinal blood vessel segmentation. The MFCTrans-net is an improvement over original U-Net, which can be summarized as (1) to better fuse the encoder and decoder features and reduce the semantic gap, we replace the skip connection in the original U-net by the Channel-wise Cross Fusion Transformer(CCT); (2) two side paths are added to the U-Net which allow the network to capture features at multiple scales; (3) a novel loss function is also proposed which focuses on the topology integrity of vessel meanwhile maintaining pixel segmentation accuracy. The proposed network has been developed and evaluated in the DRIVE, CHASE-DB1 and IOSTAR datasets, which offer a manual segmentation of the vascular tree by each of its images. The performance of our method is evaluated in terms of visual effects and quantitative evaluation metrics on these four publicly available datasets with comparison to several representative methods. Furthermore, we use the proposed method to segment a collection of the experimentally obtained retinal blood vessel images with poor quality. The experimental results demonstrate the performance of our proposed method." @default.
- W4382246144 created "2023-06-28" @default.
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- W4382246144 date "2023-06-27" @default.
- W4382246144 modified "2023-09-24" @default.
- W4382246144 title "MFCTrans-net: a multi-scale fusion and channel transformer net for retinal vessel segmentation" @default.
- W4382246144 doi "https://doi.org/10.1117/12.2682910" @default.
- W4382246144 hasPublicationYear "2023" @default.
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