2K Fold Net and Feature Enhanced 4 Fold Net for Medical Image Segmentation
2K Fold Net and Feature Enhanced 4 Fold Net for Medical Image Segmentation
复制标题
用于医学图像分割的 2K 折叠网络和特征增强型 4 折叠网络
DOI：
10.1016/j.patcog.2022.108625
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发表时间：
2022
影响因子：
8
通讯作者：
Jianfei Dong
中科院分区：
文献类型：
--
作者：
Yunchu Zhang;Jianfei Dong
For segmenting medical images, U-Net has become a popular and effective tool. However, it also has some shortcomings in segmenting fuzzy boundaries and eliminating interferences. Improvements of the original U-Net have been proposed by many authors, resulting in many variants such as MultiResUNet, DoubleU-Net and W-Net. Based on the common characteristics of these structures, we propose in this work a generalized structure by multiplying the folds of a fully convolutional network (FCN) for even more times, and thus name it as “2K-Fold-Net”. The more folds in this structure provide more freedoms to create cross links between the neighboring folds. The influence of the fold-pair number K on its performance is also studied. The realizations with K up to 6 are compared to three other variants of cascaded U-Nets using the CVC-ClinicDB dataset. Then the special case “4-Fold-Net” is further empowered with the feature enhancing functionalities recently seen in the attention-aware feature enhancement method. This new net is hence named as “Enhanced-Feature-4-Fold-Net”, abbreviated as “EF 3-Net”. Finally, 2K-Fold-Net and EF 3-Net have been compared with U-Net, SegNet, DoubleU-Net, MultiResUNet and its variants using four challenging medical image datasets. The results have demonstrated that the proposed nets outperform the other variants of U-Net, even with slightly lower amount of parameters. The code is available on: https://github. com/raik7/EF3-Net.
