Wu Yun, Shen Huaiyan, Tan Yaya, Shi Yucheng
State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China.
College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
Int J Comput Assist Radiol Surg. 2022 Oct;17(10):1915-1922. doi: 10.1007/s11548-022-02653-9. Epub 2022 Jun 8.
Due to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment liver tumors from CT images. To address these problems, we propose an end-to-end segmentation method for liver tumors.
The method uses a cascade structure to improve the network's extraction of information. First, the Side-output Feature Fusion Attention block is used to fuse features at different levels and combine with attention mechanism to focus on important information. Then, the Atrous Spatial Pyramid Pooling Attention block is used to extract multi-scale semantic features. Finally, the Multi-scale Prediction Fusion block is used to fully fused the features captured at each layer of the network.
To verify the performance of the proposed model and the effectiveness of each module, we evaluate it on LiTS and 3DIRCADb datasets and obtained Dice per Case of 0.665 and 0.719, respectively, and Dice Global of 0.812 and 0.784, respectively.
The proposed method is compared with the basic model 3D U-Net, as well as some mainstream methods based on U-Net variants, and our method achieves better performance on the liver tumor segmentation task and is superior to most segmentation algorithms.
由于肝脏肿瘤结构复杂且与正常组织对比度低，从CT图像中准确分割肝脏肿瘤仍然是一项具有挑战性的任务。为了解决这些问题，我们提出了一种用于肝脏肿瘤的端到端分割方法。
该方法使用级联结构来改进网络对信息的提取。首先，使用侧输出特征融合注意力模块在不同层级融合特征，并结合注意力机制聚焦重要信息。然后，使用空洞空间金字塔池化注意力模块提取多尺度语义特征。最后，使用多尺度预测融合模块对网络各层捕获的特征进行充分融合。
为验证所提模型的性能及各模块的有效性，我们在LiTS和3DIRCADb数据集上对其进行评估，分别获得了每例的Dice系数为0.665和0.719，以及全局Dice系数为0.812和0.784。
将所提方法与基础模型3D U-Net以及一些基于U-Net变体的主流方法进行比较，我们的方法在肝脏肿瘤分割任务上取得了更好的性能，优于大多数分割算法。
