UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

Code

• github.com/ZJUGiveLab/UNet-Version
  OfficialIn paperpytorch★ 717
• github.com/DaloroAT/first_break_picking
  pytorch★ 130
• github.com/hamidriasat/UNet-3-Plus
  tf★ 100
• github.com/Owais-Ansari/Unet3plus
  pytorch★ 10
• github.com/mindspore-ai/models/tree/master/research/cv/Neighbor2Neighbor
  mindspore★ 0
• github.com/MindSpore-scientific-2/code-5/tree/main/D-Unet_Mindspore
  mindspore★ 0

Abstract

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

Tasks

Benchmark Results

Reproductions