Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis

• URL: http://arxiv.org/abs/2509.19711v1
• Date: Wed, 24 Sep 2025 02:44:53 GMT
• Title: Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
• Authors: Jiesi Hu, Yanwu Yang, Zhiyu Ye, Chenfei Ye, Hanyang Peng, Jianfeng Cao, Ting Ma,
• Abstract summary: In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training.<n>We propose textbf SynthICL, a novel data synthesis framework built upon domain randomization.<n>Our work helps mitigate the data bottleneck for ICL-based segmentation, paving the way for robust models.
• Score: 12.827413884809644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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