  In this paper, we present UNet++, a new, more powerful architecture for
medical image segmentation. Our architecture is essentially a deeply-supervised
encoder-decoder network where the encoder and decoder sub-networks are
connected through a series of nested, dense skip pathways. The re-designed skip
pathways aim at reducing the semantic gap between the feature maps of the
encoder and decoder sub-networks. We argue that the optimizer would deal with
an easier learning task when the feature maps from the decoder and encoder
networks are semantically similar. We have evaluated UNet++ in comparison with
U-Net and wide U-Net architectures across multiple medical image segmentation
tasks: nodule segmentation in the low-dose CT scans of chest, nuclei
segmentation in the microscopy images, liver segmentation in abdominal CT
scans, and polyp segmentation in colonoscopy videos. Our experiments
demonstrate that UNet++ with deep supervision achieves an average IoU gain of
3.9 and 3.4 points over U-Net and wide U-Net, respectively.
