Deep learning (DL) methods have been widely applied to 3D coronary artery segmentation. However, due to the large data volume of 3D CT images, efficient GPU memory utilization has become a major bottleneck for the practical deployment of current DL segmentation models. In addition, effectively capturing tubular structure information while suppressing background noise in feature maps remains a significant challenge. To address these issues, we propose a Memory-Efficient U-Net (ME-UNet)—a lightweight U-Net variant that removes residual connections in each block and introduces two key components: the Uncertainty Skip Connection (USC) and Dimensional Fusion Attention (DFA). Experimental results show that ME-UNet not only reduces GPU memory consumption but also achieves state-of-the-art segmentation performance. The source code is available at https://github.com/hd1437/ME-UNet . • We propose ME-UNet, a lightweight, memory-efficient 3D CCTA vessel segmentation model. • Dimensional Fusion Attention for efficient global–local context modeling is introduced. • Uncertainty Skip Connections suppress noise and enhance robustness. • On ImageCAS, ME-UNet surpasses UNet++ in AUC (+0.011), DSC (+0.007), IoU (+0.013). • GPU memory is reduced to ∼ 4.7 GB vs 18.4 GB (UNet++), with favorable runtime characteristics.
Quan et al. (Sun,) studied this question.