Fig. 4:

Comparison between U-Net, UNet+, and UNet++ when applied to the state-of-the-art backbones for the tasks of neuronal structure, cell, nuclei, brain tumor, and liver segmentation. UNet++, trained with deep supervision, consistently outperforms U-Net across all backbone architectures and applications under study. By densely connecting the intermediate layers, UNet++ also yields higher segmentation performance than UNet+ in most experimental configurations. The error bars represent the 95% confidence interval and the number of * on the bridge indicates the level of significance measured by p-value (“n.s.” stands for “not statistically significant”).