用合成数据提升医疗图像分割的上下文学习能力
Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
- 基于领域随机化生成多样化且符合解剖先验的医学图像
- 在4个独立数据集上实现平均Dice提升63%，泛化能力显著增强
- 适合需要少样本训练的医疗影像研究者使用
上下文学习（ICL）在通用医疗图像分割中的兴起，对大规模、多样化的训练数据提出了前所未有的需求，加剧了长期存在的数据稀缺问题。尽管数据合成提供了一种有前景的解决方案，但现有方法往往难以同时实现高数据多样性和适用于医学数据的领域分布。为此，我们提出 extbf{SynthICL}，一种基于领域随机化的新型数据合成框架。SynthICL通过利用真实数据集中的解剖先验保证真实性，生成多样化的解剖结构以覆盖广泛的数据分布，并显式建模个体间差异，从而生成适合ICL使用的数据集。在四个独立测试数据集上的大量实验验证了该框架的有效性，使用我们合成数据训练的模型在平均Dice上最高提升63%，并对未见解剖领域展现出显著增强的泛化能力。本工作有助于缓解基于ICL分割的数据瓶颈，推动鲁棒模型的发展。代码与生成数据集已公开于https://github.com/jiesihu/Neuroverse3D。
原文摘要 · Abstract (English)
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating the long-standing problem of data scarcity. While data synthesis offers a promising solution, existing methods often fail to simultaneously achieve both high data diversity and a domain distribution suitable for medical data. To bridge this gap, we propose \textbf{SynthICL}, a novel data synthesis framework built upon domain randomization. SynthICL ensures realism by leveraging anatomical priors from real-world datasets, generates diverse anatomical structures to cover a broad data distribution, and explicitly models inter-subject variations to create data cohorts suitable for ICL. Extensive experiments on four held-out datasets validate our framework's effectiveness, showing that models trained with our data achieve performance gains of up to 63\% in average Dice and substantially enhanced generalization to unseen anatomical domains. Our work helps mitigate the data bottleneck for ICL-based segmentation, paving the way for robust models. Our code and the generated dataset are publicly available at https://github.com/jiesihu/Neuroverse3D.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。