用于高效眼前节分割的DINOv3特征的步注意力细化

中文摘要 AI 辅助

眼前节（Anterior Eye Segment, AES）分割是眼部生物识别技术及新兴临床图像分析应用的关键组成部分。然而，医疗场景中异构的采集条件和有限的标注数据，阻碍了现有方法的鲁棒性与泛化能力。DINOv3等基础模型（Foundation Models, FMs）具备强大的迁移能力，但将其表示高效适配密集预测任务仍具挑战性。本研究针对临床场景中的鲁棒AES分割问题，提出一种基于蒸馏DINOv3 ViT-Small主干的轻量型架构，引入步注意力特征细化模块，在卷积解码前逐步适配多级Transformer表示，以少量参数高效利用预训练特征。在包含8种眼科采集协议、标注7个解剖类别的333张临床采集AES图像的私有数据集上评估，与包括基于DINOv3的方法在内的卷积及Transformer基线方法相比，所提方法在全微调时达到85.55%的平均交并比（mIoU），取得最优整体性能，且在4个未见过的公开AES分割数据集上展现出最强的域偏移鲁棒性。这些结果为临床场景下的鲁棒AES分割建立了强基线，并凸显了解码器设计对有效适配基础模型表示至医学分割任务的重要性。

英文摘要

Anterior eye segment (AES) segmentation is a key component of both ocular biometrics and emerging clinical image analysis applications. However, heterogeneous acquisition conditions and limited annotations in medical settings hinder the robustness and generalization of existing methods. Foundation models (FMs) such as DINOv3 offer strong transfer capabilities, but efficiently adapting their representations to dense prediction tasks remains challenging. In this study, we investigate robust AES segmentation in clinical settings, and propose a lightweight architecture built upon a distilled DINOv3 ViT-Small backbone. We introduce a step-attention feature refinement module that progressively adapts multi-level transformer representations before convolutional decoding, enabling efficient exploitation of pretrained features with few parameters. We evaluate the proposed approach on a private dataset of 333 clinically acquired AES images spanning eight ophthalmic acquisition protocols and annotated for seven anatomical classes. Compared with convolutional and transformer-based baselines, including DINOv3-based methods, our approach achieves the best overall performance, reaching 85.55\% mIoU when fully fine-tuned. It also demonstrates the strongest robustness to domain shift across four unseen public AES segmentation datasets. These results establish a strong baseline for robust AES segmentation in clinical settings and highlight the importance of decoder design for effectively adapting FMs representations to medical segmentation tasks.

发表机构

• Polytechnique Montréal（蒙特利尔理工学院）
• LightX Innovations（LightX创新公司）
• Institut de l’Oeil des Laurentides（劳伦琴眼科研究所）

机构由 AI 辅助整理，请以论文原文为准。