用合成数据训练医疗图像分割模型，无需真实医学数据即可达到高精度。
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
- 基于合成数据生成框架，模拟真实医疗图像复杂特性
- 在9个数据集上对11个解剖结构实现超越零样本基线的分割性能
- 适合缺乏标注数据的医疗AI研究者快速部署通用分割模型
像Segment Anything Model（SAM）这样的基础模型在自然图像零样本分割上表现优异，但在医学图像分割中因纹理、对比度和噪声差异而表现不佳。医学图像标注成本高且需专业领域知识，限制了大规模标注数据的获取。为此，我们提出SynthFM，一种模拟医学图像复杂特性的合成数据生成框架，使基础模型可在无真实医学数据情况下进行适配。利用SAM的预训练编码器，并在SynthFM生成的数据集上从头训练解码器，我们在9个数据集（CT、MRI、超声）上的11个解剖结构上进行了评估。结果表明，SynthFM在不同提示设置及分布外数据集上均优于SAM、MedSAM等零样本基线方法。
原文摘要 · Abstract (English)
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in texture, contrast, and noise. Annotating medical images is costly and requires domain expertise, limiting large-scale annotated data availability. To address this, we propose SynthFM, a synthetic data generation framework that mimics the complexities of medical images, enabling foundation models to adapt without real medical data. Using SAM's pretrained encoder and training the decoder from scratch on SynthFM's dataset, we evaluated our method on 11 anatomical structures across 9 datasets (CT, MRI, and Ultrasound). SynthFM outperformed zero-shot baselines like SAM and MedSAM, achieving superior results under different prompt settings and on out-of-distribution datasets.
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