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

• URL: http://arxiv.org/abs/2004.08790v1
• Date: Sun, 19 Apr 2020 08:05:59 GMT
• Title: UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
• Authors: Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Jian Wu
• Abstract summary: We propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions. The proposed method is especially benefiting for organs that appear at varying scales.
• Score: 20.558512044987125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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