Automatic Channel Pruning of U-Net Applied to Retinal Blood Vessels Segmentation

The U-Net model is widely utilized in medical image segmentation, yielding satisfactory results. However, retinal vascular image segmentation encounters an issue of model parameter redundancy. In our study, we propose a U-Net network pruning method utilizing a genetic optimization algorithm. The objective is to streamline the U-Net network structure by channel pruning, reducing the network’s parameter count. Unlike conventional channel pruning methods, our algorithm prunes channels in each U-Net layer, avoiding selective pruning of “non-important” channels. Experimental evaluation on the DRIVE dataset produced a pruned model structure, later validated on STARE and CHASE_DB1 datasets to confirm accuracy. Analysis of experimental data indicates the pruned model is more lightweight than the original U-Net, with a 37.69% reduction in parameters and a 22.5% reduction in channels. Segmentation results show no significant differences. The proposed algorithm effectively trims U-Net network parameters without negative impacts on segmentation. Compared to manually pruned structures, our model demonstrates advantages in retinal image segmentation.