The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

中文翻译：

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基于 U-Net 的密集连接网络用于医学图像分割

U-Net 已成为近年来医学图像分割中最流行的结构。尽管其在医学图像分割方面的表现出色，但大量实验表明，当分割目标的大小发生变化以及不同分割形式的目标与背景之间出现不平衡时，经典的 U-Net 网络架构似乎不足。为了改进 U-Net 网络架构，我们在本文中开发了一种称为密集连接 U-Net (DenseUNet) 网络的新架构。所提出的 DenseUNet 网络采用密集块来提高特征提取能力，并采用多特征融合块融合不同级别的特征图来提高特征提取的准确性。此外，鉴于交叉熵和骰子损失函数的优点，提出了一种新的DenseUNet网络损失函数来处理目标和背景之间的不平衡。最后，我们测试了提出的 DenseUNet 网络，并将其与多分辨率 U-Net (MultiResUNet) 和三个不同数据集上的经典 U-Net 网络进行了比较。实验结果表明，与 MultiResUNet 和经典 U-Net 网络相比，DenseUNet 网络具有显着的性能。