PRORED: a hybrid transformer framework with progressive refinement decoding for segmenting dynamic speech MRI
Ying He1,2, Qianni Zhang1,2, Marc E Miquel3,4
1School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, United Kingdom.
BJR Artificial Intelligence
|May 1, 2026
Summary
A new deep learning model accurately segments dynamic speech MRI, improving velopharyngeal closure studies. This hybrid transformer network offers enhanced precision for medical image analysis.
Area of Science:
- Medical imaging
- Artificial intelligence
- Speech science
Background:
- Dynamic MRI of the upper vocal tract is crucial for speech analysis.
- Manual segmentation of speech MRI data is labor-intensive and time-consuming.
- Development of automatic segmentation methods is essential for efficient analysis.
Purpose of the Study:
- To introduce a novel hybrid transformer network for accurate segmentation of dynamic speech MRI.
- To improve the analysis of speech production and velopharyngeal closure.
- To develop a generalizable model for medical image segmentation.
Main Methods:
- A deep learning-based decoder model, Progressively Refinement Decoding (PRORED), was developed.
- A directional field (DF) module was incorporated to capture and refine feature contour details.
- The DF module was integrated at multiple decoder stages for progressive feature enhancement.
Main Results:
- The model achieved a 97.78% Dice coefficient and 6.84 mm Hausdorff distance on speech MRI data.
- It demonstrated superior efficiency in identifying closure patterns compared to baseline methods.
- The model achieved a 91.90% Dice score on a cardiac dataset, indicating generalizability.
Conclusions:
- The proposed model enables more accurate segmentation of speech MRI, particularly for velopharyngeal closure studies.
- The model's strong performance on a cardiac dataset highlights its generalizability across different medical imaging applications.
- This work presents the first model combining vision transformers and progressive refinement decoding for dynamic speech MRI segmentation.
