  Recently, a growing interest has been seen in deep learning-based semantic
segmentation. UNet, which is one of deep learning networks with an
encoder-decoder architecture, is widely used in medical image segmentation.
Combining multi-scale features is one of important factors for accurate
segmentation. UNet++ was developed as a modified Unet by designing an
architecture with nested and dense skip connections. However, it does not
explore sufficient information from full scales and there is still a large room
for improvement. In this paper, we propose a novel UNet 3+, which takes
advantage of full-scale skip connections and deep supervisions. The full-scale
skip connections incorporate low-level details with high-level semantics from
feature maps in different scales; while the deep supervision learns
hierarchical representations from the full-scale aggregated feature maps. The
proposed method is especially benefiting for organs that appear at varying
scales. In addition to accuracy improvements, the proposed UNet 3+ can reduce
the network parameters to improve the computation efficiency. We further
propose a hybrid loss function and devise a classification-guided module to
enhance the organ boundary and reduce the over-segmentation in a non-organ
image, yielding more accurate segmentation results. The effectiveness of the
proposed method is demonstrated on two datasets. The code is available at:
github.com/ZJUGiveLab/UNet-Version
