# StrDiSeg: Adapter-Enhanced DINOv3 for Automated Ischemic Stroke Lesion Segmentation

## TL;DR

StrDiSeg improves stroke lesion segmentation in medical images by adapting a vision foundation model with lightweight adapters, achieving strong results across different imaging modalities.

## Contribution

Introduces StrDiSeg, an adapter-based framework for efficient and effective medical image segmentation using DINOv3.

## Key findings

- StrDiSeg achieved a Dice score of 0.516 on the AISD dataset and 0.824 on the ISLES22 dataset.
- The method outperformed baseline models and showed robustness across different clinical imaging modalities.
- Adapter-based fine-tuning is a computationally efficient strategy for leveraging large vision models in medical image segmentation.

## Abstract

Deep vision foundation models such as DINOv3 offer strong visual representation capacity, but their direct deployment in medical image segmentation remains difficult due to the limited availability of annotated clinical data and the computational cost of full fine-tuning. This study proposes an adaptation framework called StrDiSeg that integrates lightweight bottleneck adapters between selected transformer layers of DINOv3, enabling task-specific learning while preserving pretrained knowledge. An attention-enhanced U-Net decoder with multi-scale feature fusion further refines the representations. Experiments were performed on two publicly available ischemic stroke lesion segmentation datasets—AISD (Non Contrast CT) and ISLES22 (DWI). The proposed method achieved Dice scores of 0.516 on AISD and 0.824 on ISLES22, outperforming baseline models and demonstrating strong robustness across different clinical imaging modalities. These results indicate that adapter-based fine-tuning provides a practical and computationally efficient strategy for leveraging large pretrained vision models in medical image segmentation.