基础,高级和传统深度学习模型对高极化气体MRI肺部细分的比较分析:在数据受限制的场景中强大的性能
Ramtin Babaeipour1, Matthew S Fox2,3,4, Grace Parraga1,4,5
1School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, Canada.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
基础和先进的人工智能模型擅长对肺部疾病的超极化气体MRI进行细分,即使数据有限. 传统模型扎,突出了医学成像中先进架构的需要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 需要先进的诊断工具.
- 超极化气体MRI (使用He和Xe) 提供非侵入性肺功能评估.
- 精确的细分对于分析肺部MRI数据至关重要.
研究的目的:
- 为了比较基础,高级和传统的深度学习模型对高极化气体MRI细分的性能.
- 在不同的数据可用性场景下评估模型性能 (100%降至10%).
- 为数据稀缺的医学成像应用程序确定强大的细分策略.
主要方法:
- 分段任何模型,MedSAM,UniRepLKNet,TransXNet,UNet,FPN和DeepLabV3.3的比较分析
- 在四个数据缩小级别中使用子相似系数 (DSC) 评估细分性能.
- 使用p值进行绩效比较评估的统计显著性.
主要成果:
- 基础模型和高级模型在所有数据场景中显示了统计学上相当的性能 (p > 0.01).
- 基础模型和高级模型在数据限制下显著优于传统模型 (p < 0.001).
- 在10%的培训数据中,基础/高级模型保持DSC>0.86,而传统模型失败了.
结论:
- 具有大型有效受体场的架构对于具有有限数据的医疗成像细分至关重要.
- 基础和高级模型为在资源有限的环境中民主化先进的医学成像分析提供了有希望的解决方案.
- 这项研究强调了新的AI架构在具有挑战性的临床数据场景中的适应性和稳定性.
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