seg2med:从人工解剖学到多式联络医疗图像的桥梁
Zeyu Yang1,2, Zhilin Chen1,2,3, Yipeng Sun4
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Physics in medicine and biology
|December 2, 2025
概括
分段框架从解剖学数据生成高质量的医学图像 (CT和MR). 这种以解剖学为导向的方法增强了跨模式合成,并支持医疗成像中的AI开发.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算解剖学的计算解剖学
背景情况:
- 多模式医学图像合成对于各种临床应用至关重要.
- 在不同的模式 (CT,MR) 中生成高保真图像仍然具有挑战性.
- 解剖学信息可以作为图像合成的强大先验.
研究的目的:
- 介绍seg2med,一个模块化框架用于解剖驱动的多式联络医疗图像合成.
- 为了实现计算机断层扫描 (CT) 和磁共振 (MR) 图像的高保真生成.
- 为了利用结构化解剖学先验进行跨模式的图像转换.
主要方法:
- 来自真实患者数据的解剖图,数字幻象 (XCAT) 和合成受试者.
- 物理合成模拟器将解剖面具转换为特定模式的成像先验.
- 用解剖学先验训练的二通道条件无声扩散概率模型用于图像生成.
主要成果:
- 与真实数据相比,CT (0.94±0.02) 和MR (0.82±0.12) 的结构相似度指数 (SSIM) 达到较高.
- 在跨模式转换任务中表现强 (例如,MR→CT的SSIM为0.91±0.03).
- 合成CT图像显示了高解剖准确度,关键器官的Dice系数>0.90.
结论:
- seg2med为解剖驱动的多式联络医疗图像合成提供了一个强大的框架.
- 该系统可促进高保真度跨模式图像生成和转换.
- 结果支持seg2med对数据集增强和医学成像中的解剖意识AI开发的实用性.
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