为了改善未破裂的内动脉瘤的决策,使用自动细分从MRA-TOF与代伪标签的自动细分
Anouk S Verschuur1, Jiaxin Zhang2, Maarten J Kamphuis2
1From the Image Sciences Institute (A.S.V., J.Z., M.J.K., C.M.W.T., I.C.V.D.S.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands and CUBRIC, School of Physics and Astronomy (C.M.W.T.), Cardiff University, CF10 3AT, Cardiff, United Kingdom. a.s.verschuur@alumnus.utwente.nl.
AJNR. American journal of neuroradiology
|February 14, 2026
概括
深度学习模型DIVA-seg从MRA-TOF扫描中准确地划分内血管和动脉瘤. 这种自动化方法通过改善3D形态评估和减少观察者变异性,显示出临床使用的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 神经成像是一种神经成像.
背景情况:
- 准确的内动脉瘤评估对于治疗规划和风险分层至关重要.
- 手动细分是耗时的,容易引起观察者之间的和观察者内部的变化.
- 现有的自动化方法往往缺乏准确性,稳定性或足够的验证.
研究的目的:
- 开发和验证DIVA-seg,一种基于深度学习的方法,用于使用MRA-TOF进行内血管和动脉瘤细分.
- 为了实现准确的3D形态评估和支持临床决策.
- 为满足对可靠和可通用的自动化动脉瘤细分工具的未满足需求.
主要方法:
- 使用了三个MRA-TOF数据集:标记 (培训/测试),未标记 (伪标记) 和外部验证.
- 采用了一种代的nnU-Net方法,用于在未标记的数据上生成伪标签.
- 训练了第二个nnU-Net模型,包含伪标记和原标记数据;使用DSC,豪斯多夫距离和3D形态测量来评估性能.
主要成果:
- 在血管 (0.925) 和动脉瘤 (0.880) 方面,DIVA-seg实现了高的内部子相似系数 (DSC).
- 外部验证显示了强的性能,血管的DSC为0.899,动脉瘤的DSC为0.861.
- 平均豪斯多夫距离低于0.7mm;在3D形态测量中观察到高一致性,在某些指标中观察到较小的比例偏差.
结论:
- DIVA-seg提供了对内血管和动脉瘤的准确和可靠的细分.
- 该模型在内部和外部数据集中展示了一致的性能.
- 在3D形态测量方面达成的高度一致性表明,对于简化动脉瘤监测和决策的临床实用性是显著的.
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