StrDiSeg: Adapter-Enhanced DINOv3 for Automated Ischemic Stroke Lesion Segmentation
Qiong Chen1, Donghao Zhang2, Yimin Chen2
1Ultrasound Diagnosis Department, Wuhan No. 1 Hospital, Wuhan 430000, China.
Bioengineering (Basel, Switzerland)
|February 27, 2026
Summary
This study introduces StrDiSeg, an efficient method for medical image segmentation using deep vision models. It enables effective adaptation of large pretrained models like DINOv3 for tasks like ischemic stroke lesion segmentation.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Computer Vision
Background:
- Deep vision foundation models (e.g., DINOv3) possess strong visual representation capabilities.
- Direct application in medical image segmentation is hindered by limited annotated data and high computational costs of full fine-tuning.
Purpose of the Study:
- To propose StrDiSeg, an adaptation framework for efficient fine-tuning of deep vision models in medical image segmentation.
- To enable task-specific learning while preserving pretrained knowledge from models like DINOv3.
Main Methods:
- Integration of lightweight bottleneck adapters within transformer layers of DINOv3.
- Utilizing an attention-enhanced U-Net decoder with multi-scale feature fusion.
- Experiments conducted on ischemic stroke lesion segmentation datasets (AISD and ISLES22).
Main Results:
- StrDiSeg achieved Dice scores of 0.516 on AISD (Non-Contrast CT) and 0.824 on ISLES22 (DWI).
- The method outperformed baseline models in segmenting ischemic stroke lesions.
- Demonstrated robustness across different clinical imaging modalities.
Conclusions:
- Adapter-based fine-tuning offers a practical and computationally efficient strategy for medical image segmentation.
- Leveraging large pretrained vision models is feasible for clinical applications with methods like StrDiSeg.
- The framework shows promise for improving segmentation accuracy in challenging medical imaging tasks.
