Related Experiment Video For 3D U-Net
Updated: Sep 20, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net
Yun Wu1,2, Huaiyan Shen3, Yaya Tan2
1State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China.
Purpose:
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.
Methods:
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.
Result:
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.
Conclusion:
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.
