Purpose

Deep learning has recently shown its outstanding performance in biomedical image semantic segmentation. Most biomedical semantic segmentation frameworks comprise the encoder–decoder architecture directly fusing features of the encoder and the decoder by the way of skip connections. However, the simple fusion operation may neglect the semantic gaps which lie between these features in the decoder and the encoder, hindering the effectiveness of the network.

Methods

Dense gate network (DG-Net) is proposed for biomedical image segmentation. In this model, the Gate Aggregate structure is utilized to reduce the semantic gaps between features in the encoder and the corresponding features in the decoder, and the gate unit is used to reduce the categorical ambiguity as well as to guide the low-level high-resolution features to recover semantic information. Through this method, the features could reach a similar semantic level before fusion, which is helpful for reducing semantic gaps, thereby producing accurate results.

Results

Four medical semantic segmentation experiments, based on CT and microscopy images datasets, were performed to evaluate our model. In the cross-validation experiments, the proposed method achieves IOU scores of 97.953%, 89.569%, 81.870% and 76.486% on these four datasets. Compared with U-Net and MultiResUNet methods, DG-Net yields a higher average score on IOU and Acc.

Conclusion

The DG-Net is competitive with the baseline methods. The experiment results indicate that Gate Aggregate structure and gate unit could improve the performance of the network by aggregating features from different layers and reducing the semantic gaps of features in the encoder and the decoder. This has potential in biomedical image segmentation.

中文翻译：

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用于生物医学图像分割的密集门网络。

目的

深度学习最近显示了其在生物医学图像语义分割中的出色表现。大多数生物医学语义分割框架都包含编码器-解码器体系结构，通过跳过连接的方式直接将编码器和解码器的功能融合在一起。但是，简单的融合操作可能会忽略位于解码器和编码器中这些功能之间的语义差距，从而阻碍了网络的有效性。

方法

提出了密集门网络（DG-Net）用于生物医学图像分割。在此模型中，Gate Aggregate结构用于减少编码器中的特征与解码器中相应特征之间的语义鸿沟，而Gate单元用于减少分类歧义并指导低级别的解析功能以恢复语义信息。通过这种方法，特征可以在融合之前达到相似的语义水平，这有助于减少语义差距，从而产生准确的结果。

结果

基于CT和显微镜图像数据集进行了四个医学语义分割实验，以评估我们的模型。在交叉验证实验中，该方法在这四个数据集上的IOU得分分别为97.953％，89.569％，81.870％和76.486％。与U-Net和MultiResUNet方法相比，DG-Net在IOU和Acc上产生更高的平均分数。

结论

DG-Net与基准方法相比具有竞争力。实验结果表明，Gate Aggregate结构和Gate单元可以通过聚合来自不同层的特征并减少编码器和解码器中特征的语义间隙来提高网络性能。这在生物医学图像分割中具有潜力。