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.