Figure 3.

The network architecture of UNet++. UNet++ consists of encoder and decoder connecting through a series of nested dense convolutional blocks. The semantic gap between the feature maps of the encoder and decoder is bridged prior to fusion. The encoder extract features by down-sampling; the decoder map features to the original image by up-sampling, make classification by pixels, and thus achieve the purpose of segmentation. Resnet-50 was used as backbone of UNet++, and all the pre-training parameters of ResNet-50 are loaded to UNet++.