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生成型数据引擎基础模型用于通用少数拍摄的2D血管图像细分2D血管图像细分

Rongjun Ge¹, Xin Li², Yuxing Liu²

¹School of Instrument Science and Engineering, Southeast University, Nanjing, China.

Medical image analysis

|February 18, 2026

概括

UniVG是一种生成基础模型,通过合成多样化的图像来增强少数拍摄的血管细分. 这种方法显著降低了数据注释成本,同时实现了与完全监督的方法可比的性能.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

对于二维血管细分的深度学习在临床上是有价值的,但受到稀缺的注释数据的限制.
由于广泛的培训需求和成像复杂性,开发通用的几次射击血管细分模型具有挑战性.

研究的目的:

介绍UniVG,这是一个用于通用少数拍摄2D血管图像分割的生成基础模型.
通过实现多样化和现实的血管图像的合成来解决数据稀缺问题.

主要方法:

通过重组结构特征,UniVG利用组合学习来合成各种血管图像和标签.
它采用了少量拍摄的生成适应,以微调具有最小数据的模型,将合成和真实数据领域相结合.
一个大型数据集,UniVG-58K (58,689个图像跨越5种模式),被创建用于生成性预训练.

主要成果:

在11个船舶细分任务中,UniVG在每项任务中只使用5张标记图像,实现了与完全监督模型相似的性能.
显著降低了数据收集和注释成本.
在5种成像模式中验证了有效性.

结论:

UniVG 提供了一种强大的解决方案,用于几次射击的血管细分,克服数据限制.
生成型基础模型方法促进了跨不同模式的可转移细分.
公共可用的代码和数据集鼓励进一步的研究和应用.

关键词:

41A0505 其他其他 41A1010 其他其他 65D0505 这是一个很大的问题.65D1717 这是一本书.有几次射击学习学习.基金会模型基金会模型一个生成数据引擎.血管细分系统的细分

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