Liver segmentation is critical for the location and diagnosis of liver cancer. The variant of U-Net network with skip connections has become popular in the medical image segmentation field. However, these variant networks tend to fuse semantically dissimilar feature maps via simple skip connections between encoder and decoder path. We argue that the network learning task would be handled easily when the feature maps from the encoder-decoder path are semantically similar. The fusion of semantically dissimilar feature maps can cause gaps between feature maps. Hence, the proposed method in this paper is to obtain semantically similar feature maps, alleviate the semantic gaps caused by simple skip connections, and improve segmentation accuracy. In this paper, we proposed a new U-Net architecture named Multi-Scale Nested U-Net (MSN-Net). The MSN-Net consists of Res-block and MSCF-block. The Res-block with the bottleneck layer is used to make the network deeper and avoid gradient disappearance. To alleviate the semantic gaps, we redesign a novel skip connection. The novel skip connection consists of MSCF-block and dense connections. The MSCF-block combines High-level and Low-level features and Multi-scale semantic information to obtain more representative features. The densely connections are adopted between MSCF-blocks. In addition, we use a weighted loss function which consists of cross-entropy loss and Dice loss. The proposed method is evaluated on the dataset of MICCAI 2017 LiTS Challenge. The results of experiment demonstrate that, MSN-Net can effectively alleviate the semantic gaps and outperform other state-of-the-art methods. The method proposed with the novel skip connections can effectively alleviate the semantic gaps between encoder and decoder path and improve segmentation accuracy of the network.

中文翻译：

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MSN-Net：用于肝脏分割的多尺度上下文嵌套 U-Net

肝脏分割对于肝癌的定位和诊断至关重要。具有跳跃连接的 U-Net 网络变体在医学图像分割领域已经变得流行。然而，这些变体网络倾向于通过编码器和解码器路径之间的简单跳过连接来融合语义上不同的特征图。我们认为，当来自编码器-解码器路径的特征图在语义上相似时，网络学习任务将很容易处理。语义不同的特征图的融合会导致特征图之间的差距。因此，本文提出的方法是获得语义相似的特征图，缓解简单跳过连接造成的语义差距，提高分割精度。在本文中，我们提出了一种名为多尺度嵌套 U-Net (MSN-Net) 的新 U-Net 架构。MSN-Net 由 Res-block 和 MSCF-block 组成。带有瓶颈层的 Res-block 用于使网络更深，避免梯度消失。为了缓解语义差距，我们重新设计了一种新颖的跳过连接。新颖的跳过连接由 MSCF 块和密集连接组成。MSCF-block结合High-Level和Low-level特征和Multi-scale语义信息，获得更具代表性的特征。MSCF块之间采用密集连接。此外，我们使用由交叉熵损失和 Dice 损失组成的加权损失函数。所提出的方法在MICCAI 2017 LiTS Challenge的数据集上进行了评估。实验结果表明，MSN-Net 可以有效地缓解语义差距并优于其他最先进的方法。