基于双分支编码的闭环分割网络

Transformer模型中,卷积视觉转换器(CvT)具备同时提取图像的局部和全局特征的能力而受到关注.对于腹部器官分割问题,为了解决CNN模型分割目标边界轮廓模糊不清的问题,提出了一种新颖的基于CvT和CNN的双分支闭环分割模型DBLNet.模型利用形状先验和预测结果的分割轮廓显式监督并指导网络学习过程.模型包括:轮廓提取编码模块(CEE)、边界形状分割网络(BSSN)和闭环结构. CEE模块首次利用改造的3D CvT和3D门控卷积层(GCL)捕获多层级轮廓特征,并辅助BSSN训练. BSSN模块设计形状特征融合模块(SFF)同时捕获目标区域和轮廓特征,推动CEE训练拟合.闭环结构使得双分支的分割结果互相反馈并辅助对方的训练.DBLNet在BTCV排行榜上测试,平均Dice得分为0.878,排名第13位;在临床医院数据进行应用测试,表现出强大的性能.

Closed-loop Segmentation Network Based on Dual-branch Encoding

In the Transformer model, the convolutional vision Transformer (CvT) has caught attention for its ability to extract both local and global features from images simultaneously. However, for abdominal organ segmentation tasks, the blurry object boundaries in CNN models should be addressed. Thus, this study proposes a novel dual-branch closed-loop segmentation model DBLNet based on CvT and CNN. The model employs explicit supervision of segmented contours using shape priors and predicted results to guide the network learning. The DBLNet model includes contour extraction encoding module (CEE), boundary shape segmentation network (BSSN), and closed-loop structure. The CEE module first utilizes modified 3D CvT and 3D gated convolutional layers (GCL) to capture multi-level contour features and assist in BSSN training. The BSSN module contains a shape feature fusion (SFF) module that captures both the object region and contour features to promote CEE training convergence. The closed-loop structure allows mutual feedback of segmentation results between the dual branches, assisting each other’s training. Experimental evaluations on the BTCV benchmark show that DBLNet achieves an average Dice score of 0.878, ranking 13th. Application tests on clinical hospital data demonstrate the strong performance of the proposed model.