基于DBCNet的TOF-MRA中脑动脉树区域自动分割方法

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张嘉骏¹,鲁宇澄²,鲍奕仿²,李郁欣²,耿辰^3,^4,^#(

),胡伏原^1,^§(

),戴亚康^1,^3,^*(

)

An Automatic Segmentation Method of Cerebral Arterial Tree in TOF-MRA Based on DBCNet

ZHANG Jiajun¹,LU Yucheng²,BAO Yifang²,LI Yuxin²,GENG Chen^3,^4,^#(

),HU Fuyuan^1,^§(

),DAI Yakang^1,^3,^*(

)

图3. DBCNet网络架构图.其中，Dec为网络的解码块，BiA和SC是本研究提出的分支解耦模块和深层特征提取模块.得到BiA模块的最终输出特征图$f_{i}^{\text{C}}$和$f_{i}^{\text{D}}$，其中C和D分别表示定位分支和分割分支，i取1、2、3代表不同的BiA模块

Fig. 3. The architecture diagram of DBCNet network. Where Dec is the decoding block of the network, BiA and SC are the branch decoupling module and deep feature extraction module proposed in this study. The final output feature maps $f_{i}^{\text{C}}$ and $f_{i}^{\text{D}}$ of the BiA module are obtained, where C and D represent the localization branch and the segmentation branch, respectively, and i takes 1, 2 and 3 to represent different BiA modules