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LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor
Laquan Li1,2, Jiaxin Tan1, Lei Yu3
1College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China.
Physics in Medicine and Biology
|October 18, 2023
Summary
This study introduces a new multi-modal tumor segmentation network for head and neck (H&N) cancers, improving detection accuracy by combining PET and CT scan data. The novel method enhances segmentation performance, crucial for effective cancer treatment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Head and neck (H&N) cancers pose a global health challenge, necessitating accurate early detection for effective treatment.
- Segmenting H&N tumors in CT images is difficult due to similar tissue densities.
- PET images offer metabolic information but lack spatial resolution, while CT provides anatomical detail.
Purpose of the Study:
- To develop an innovative multi-modal tumor segmentation method for H&N cancers.
- To leverage complementary information from PET and CT images for improved segmentation accuracy.
- To enhance the integration of multi-scale features from different imaging modalities.
Main Methods:
- Proposed a novel multi-modal tumor segmentation network (LSAM) utilizing a U-shaped architecture.
- Incorporated two key learning modules: L2-Norm self-attention and latent space feature interaction.
- Exploited PET's sensitivity and CT's anatomical information for enhanced feature interaction across modalities.
Main Results:
- Evaluated on the HECKTOR PET-CT dataset, the LSAM method demonstrated superior performance.
- Achieved high scores in key segmentation metrics: DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75).
- Outperformed existing H&N tumor segmentation techniques.
Conclusions:
- The L2-Norm Self-Attention mechanism provides scalability and reduces outlier impact.
- The Latent Space feature interaction method effectively integrates complementary multi-modal information.
- The proposed LSAM network offers a significant advancement in H&N tumor segmentation.
