Kavli Affiliate: Ting Xu
| First 5 Authors: Yanlin Xiang, Qingyuan He, Ting Xu, Ran Hao, Jiacheng Hu
| Summary:
This study proposes a 3D semantic segmentation method for the spine based on
the improved SwinUNETR to improve segmentation accuracy and robustness. Aiming
at the complex anatomical structure of spinal images, this paper introduces a
multi-scale fusion mechanism to enhance the feature extraction capability by
using information of different scales, thereby improving the recognition
accuracy of the model for the target area. In addition, the introduction of the
adaptive attention mechanism enables the model to dynamically adjust the
attention to the key area, thereby optimizing the boundary segmentation effect.
The experimental results show that compared with 3D CNN, 3D U-Net, and 3D U-Net
+ Transformer, the model of this study has achieved significant improvements in
mIoU, mDice, and mAcc indicators, and has better segmentation performance. The
ablation experiment further verifies the effectiveness of the proposed improved
method, proving that multi-scale fusion and adaptive attention mechanism have a
positive effect on the segmentation task. Through the visualization analysis of
the inference results, the model can better restore the real anatomical
structure of the spinal image. Future research can further optimize the
Transformer structure and expand the data scale to improve the generalization
ability of the model. This study provides an efficient solution for the task of
medical image segmentation, which is of great significance to intelligent
medical image analysis.
| Search Query: ArXiv Query: search_query=au:”Ting Xu”&id_list=&start=0&max_results=3