Abstract: Aiming at the problems of multi-scale lesion areas,sparse edge pixels,low contrast,as well as complex and irregular tumor shape in 3D CT images of renal cancer,this paper proposes a selective feature fusion 3D CT image segmentation network based on edge enhancement(EE-SFF U-Net).EE-SFF U-Net adopts the symmetric encoder-decoder network architecture based on U-Net,and the encoding path contains an edge enhancement module for strengthening edge information,which can effectively mine and utilize shallow feature information to alleviate the sparsity problem of edge pixels and avoid missing detection of small targets.In addition,in the skip connections of the network,a selective feature fusion module is designed to make the deep and shallow features complement each other and realize the effective aggregation of different information.Finally,a hybrid loss function with Generalized Dice Loss and Focal Loss is proposed.The dynamic weight adjustment strategy is used to realize the optimal training of the loss function,and to improve the influence of multi-scale lesions and irregular tumor shape and size.The proposed method not only ensures the accuracy of the overall localization of the lesion area,but also strengthens the mining and utilization of small target feature information,so as to improve the accuracy and robustness of segmentation.The experimental results on KiTS19 public dataset show that the proposed method performs well in various indexes and significantly improves the segmentation performance compared with other segmentation algorithms.

Key words: 3D CT segmentation of renal cancer, Edge enhancement, Selective feature fusion, 3D U-Net, Deep learning