用布局感知生成模型提升眼底血管分割的泛化能力
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
- 基于扩散模型生成带结构控制的眼底图像，保持血管布局一致
- 在多个数据集上使血管分割泛化性能提升最高8.1%
- 适合医学图像生成与跨域分割研究者参考
医学分割模型的泛化能力受限于标注数据少和成像差异大。为此，我们提出视网膜布局感知扩散模型（RLAD），一种基于扩散的可控布局生成框架。RLAD以真实图像中提取的关键布局成分（如血管、病变、视盘）为条件，确保结构保真度的同时，实现其他成分的多样性。应用于眼底荧光造影图像，通过合成配对的图像与血管分割图，利用真实血管作为条件，同时变化病变和视盘等布局成分来扩充训练集。实验表明，使用RLAD生成的数据可使视网膜血管分割的泛化能力提升最高达8.1%。此外，我们构建了包含586张人工标注眼底图像的REYIA数据集。为促进复现与创新，代码与数据集将公开共享。
原文摘要 · Abstract (English)
Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (RLAD), a novel diffusion-based framework for generating controllable layout-aware images. RLAD conditions image generation on multiple key layout components extracted from real images, ensuring high structural fidelity while enabling diversity in other components. Applied to retinal fundus imaging, we augmented the training datasets by synthesizing paired retinal images and vessel segmentations conditioned on extracted blood vessels from real images, while varying other layout components such as lesions and the optic disc. Experiments demonstrated that RLAD-generated data improved generalization in retinal vessel segmentation by up to 8.1%. Furthermore, we present REYIA, a comprehensive dataset comprising 586 manually segmented retinal images. To foster reproducibility and drive innovation, both our code and dataset will be made publicly accessible.
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