3D UNeXt:轻量级快速脑提取网络OA北大核心CSTPCD

In order to solve the drawbacks of existing brain extraction network,i.e.,complex network structure,large amounts of parameters and low inference speed,this paper proposed a novel network 3D UNeXt for fast and effective brain ex-traction.3D UNeXt greatly reduced parameters and the number of floating point operators(FLOPs),and achieved promising results with the combination of 3D convolution,3D MLP and multi-scale feature fusion.3D UNeXt used U-Net as the basic ar-chitecture and employed 3D convolutional modules to obtain local features in encoding stage.Specifically,the proposed 3D MLP module at the bottleneck stage enhanced the extraction of global features and long-range dependencies among them.In decoding stage,this paper designed a lightweight multiscale feature fusion module to effectively fuse multiscale low-level fea-tures and high-level counterparts.In detail,the 3D MLP module performed linear shift operations in three different axes to ob-tain global receptive fields from different dimension features and establish long-range dependencies among them.This paper compared 3D UNeXt with other counterparts on three datasets:IBSR,NFBS,and HTU-BrainMask.Experimental results show that the 3D UNeXt is superior over other baselines in terms of network parameters,FLOPs,inference accuracy,and inference speed.