Objectives

Accurate automatic liver segmentation has important value for subsequent tumor segmentation, diagnosis, and treatment. In this paper, a Multiscale Cascaded Feature Attention U-Net (MCFA-UNet) neural network model was proposed to solve the problem of edge detail feature loss caused by insufficient feature extraction in existing segmentation methods.

Material and methods

MCFA-UNet is a 3D segmentation network based on U-Net encoding and decoding structure. First, this paper propose a multiscale feature cascaded attention (MCFA) module, which extracts multiscale feature information through information through multiple continuous convolution paths, and use double attention to realize multiscale feature information fusion of different paths. Second, the attention-gate mechanism is used to fuse different levels of feature information, which reduces the semantic difference between coding and decoding paths. Finally, the deep supervision learning method was employed to optimize the network segmentation effect through the feature information of each hidden layer in the decoding path.

Results

MCFA-UNet was evaluated on LiTS and 3DIRCADb datasets. The Dice scores of 0.955 and 0.981 are obtained respectively. Compared with the baseline network, the segmentation accuracy is improved by 5% and 3.5%.

Conclusion

Experimental results show that MCFA-UNet has more accurate segmentation performance than baseline model and other advanced methods.

中文翻译：

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MCFA-UNet：用于肝脏分割的多尺度级联特征注意 U-Net

目标

准确的自动肝脏分割对于后续的肿瘤分割、诊断和治疗具有重要价值。本文提出了一种多尺度级联特征注意U-Net（MCFA-UNet）神经网络模型，以解决现有分割方法中特征提取不充分导致的边缘细节特征丢失问题。

材料与方法

MCFA-UNet是一种基于U-Net编解码结构的3D分割网络。首先，本文提出了多尺度特征级联注意力（MCFA）模块，通过多个连续卷积路径的信息提取多尺度特征信息，并利用双重注意力实现不同路径的多尺度特征信息融合。其次，注意力门机制用于融合不同层次的特征信息，减少了编码和解码路径之间的语义差异。最后采用深度监督学习的方法，通过解码路径中各隐藏层的特征信息，优化网络分割效果。

结果

MCFA-UNet 在 LiTS 和 3DIRCADb 数据集上进行了评估。分别获得0.955和0.981的Dice分数。与基线网络相比，分割精度分别提高了5%和3.5%。

结论

实验结果表明，MCFA-UNet 比基线模型和其他先进方法具有更准确的分割性能。