无监督的大脑MRI瘤细分通过两个阶段的图像合成
Xinru Zhang1, Ni Ou2, Chenghao Liu3
1School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China; Department of Brain Sciences, Imperial College London, London, United Kingdom.
Medical image analysis
|April 8, 2025
概括
这项研究介绍了SynthTumour,这是一种无监督的深度学习方法,用于使用合成MRI数据进行脑瘤细分. 它有效地弥合了真实和合成图像之间的域差距,改善了无需专家注释的细分精度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 深度学习在自动化脑瘤细分方面表现出色,但需要广泛的专家注释,这是昂贵和耗时的.
- 使用合成数据的无监督学习是一个有希望的替代方案,但真实数据和合成数据之间的领域差距阻碍了准确性.
研究的目的:
- 在磁共振 (MR) 图像上开发一种无监督的脑瘤细分方法,通过使用两阶段图像合成策略解决域差距.
- 为训练深度学习模型生成现实的合成脑瘤数据,从而提高分段性能而不依赖于手动注释.
主要方法:
- 建议采用两阶段的图像合成策略:首先,在合成数据上训练一个初级细分模型,并用于为真实图像生成伪标签.
- 其次,通过将真实大脑图像与这些伪标签相结合,创建现实的合成图像,以训练高级细分模型.
主要成果:
- 拟议的SynthTumour方法有效地弥合了真实和合成MR图像之间的领域差距.
- SynthTumour在脑瘤细分方面表现优于现有的无监督方法,并在五个数据集中在缺血性中风病变细分方面表现高.
结论:
- 开发的两阶段合成数据生成策略使得准确的无监督脑瘤细分成为可能.
- SynthTumour为监督方法提供了可行的高性能替代方案,减少了医疗图像分析中对专家注释的需求.
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