Abstract
Background
For the coding part of U-Net3+, the brain tumor feature extraction ability is insufficient, leading to insufficient feature fusion when sampling on the network and reducing the segmentation accuracy.
Methods
In this study, we propose an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to reduce the degradation problem caused by the increase in network depth and enhance the feature extraction ability of the encoder, which is convenient for full feature fusion when sampling on the network. Besides, we used a filter response normalization (FRN) layer instead of a batch normalization layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We explore appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network.
Results
The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus.
Conclusion
In the segmentation task of brain tumor brats2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3 +, the proposed network has smaller parameters and significantly improved accuracy.