形状的世界:可变形的几何模板,用于冠状动脉CT血管学的一次性表面网格
Rudolf L M van Herten1, Ioannis Lagogiannis1, Jelmer M Wolterink2
1Department of Biomedical Engineering and Physics, Amsterdam UMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
本研究介绍了一种数据效率高的深度学习方法,用于3D解剖面网格. 它使用几何先验来提高准确性和拓一致性,在低数据场景中表现优于现有方法.
科学领域:
- 医学成像医学成像
- 计算几何学计算几何学
- 机器学习 机器学习
背景情况:
- 传统的医学图像细分和网格化是顺序的,数据密集的,缺乏几何先前集成.
- 这导致拓不一致,并且在有限的数据下表现不佳.
研究的目的:
- 开发一种数据效率高的深度学习方法,用于直接3D解剖面网格.
- 为了提高精度和拓一致性,将几何先验纳入.
主要方法:
- 一个多分辨率图形神经网络将几何模板变形以适应对象边界.
- 为3D球形数据引入了一种新的掩盖自动编码器预训练策略.
主要成果:
- 该方法在心周,左心室 (LV) 腔和LV心肌的一次性细分方面表现优于nnUNet.
- 它还超越了在多平面重新格式化的图像上使用的其他光线细分方法.
- 网格质量与行进立方体相当或高于,三角化灵活.
结论:
- 拟议的方法为3D医疗物体表面网格提供了更准确和拓一致的方法.
- 它在数据有限的情况下特别有效,有效地利用几何先验.
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