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MDFormer: a multi-scale dense dilated transformer model for 3D medical image segmentation
Qiuxia Li1, Haiyue Zhao2, Ying Zhai1
1School of Medical and Information Engineering, Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincial Key Laboratory of Tissue Engineering (2024SSY06291), Gannan Medical University, Ganzhou, 341000, China.
Scientific Reports
|October 31, 2025
Summary
A new Transformer model, MDFormer, enhances medical image segmentation by effectively capturing multi-scale information. This improves diagnostic accuracy while maintaining computational efficiency.
Area of Science:
- Medical Imaging
- Computer Vision
- Artificial Intelligence
Background:
- Existing medical image segmentation models struggle with multi-scale information under resolution constraints.
- There is a need for efficient and accurate segmentation models for clinical diagnosis and treatment.
Purpose of the Study:
- To develop a novel model, the multi-scale dense dilated Transformer (MDFormer), for precise medical image segmentation.
- To overcome limitations in capturing multi-scale information and reduce computational costs without compromising accuracy.
Main Methods:
- Developed the multi-scale dense dilated Transformer (MDFormer) incorporating the multi-scale dense dilated self-attention (MDDSA) module.
- The MDDSA module dynamically adjusts matrix size for spatial downsampling and cross-scale aggregation, reducing computational costs.
- Evaluated the model on ACDC and Synapse datasets.
Main Results:
- Achieved a Dice Similarity Coefficient (DSC) of 92.7% on the ACDC dataset and 86.88% on the Synapse dataset.
- The MDFormer model has 37.7 million parameters and 47.39G FLOPs.
- Demonstrated statistically significant improvements in segmentation performance compared to other models via paired T-tests.
Conclusions:
- The MDFormer model significantly enhances medical image segmentation by effectively leveraging multi-scale information extraction.
- The model shows promising potential for various medical image segmentation tasks.
- Provides a foundation for future advancements in transformer-based medical imaging models.
