Andreopoulos Alexander, Tsotsos John K
York University, Department of Computer Science and Engineering, Centre for Vision Research, Toronto, Ontario, Canada.
Med Image Anal. 2008 Jun;12(3):335-57. doi: 10.1016/j.media.2007.12.003. Epub 2008 Jan 11.
We present a framework for the analysis of short axis cardiac MRI, using statistical models of shape and appearance. The framework integrates temporal and structural constraints and avoids common optimization problems inherent in such high dimensional models. The first contribution is the introduction of an algorithm for fitting 3D active appearance models (AAMs) on short axis cardiac MRI. We observe a 44-fold increase in fitting speed and a segmentation accuracy that is on par with Gauss-Newton optimization, one of the most widely used optimization algorithms for such problems. The second contribution involves an investigation on hierarchical 2D+time active shape models (ASMs), that integrate temporal constraints and simultaneously improve the 3D AAM based segmentation. We obtain encouraging results (endocardial/epicardial error 1.43+/-0.49 mm/1.51+/-0.48 mm) on 7980 short axis cardiac MR images acquired from 33 subjects. We have placed our dataset online, for the community to use and build upon.
我们提出了一个用于短轴心脏磁共振成像（MRI）分析的框架，该框架使用形状和外观的统计模型。该框架整合了时间和结构约束，并避免了此类高维模型中固有的常见优化问题。第一个贡献是引入了一种在短轴心脏MRI上拟合三维主动外观模型（AAM）的算法。我们观察到拟合速度提高了44倍，且分割精度与高斯-牛顿优化相当，高斯-牛顿优化是此类问题中使用最广泛的优化算法之一。第二个贡献涉及对分层二维+时间主动形状模型（ASM）的研究，该模型整合了时间约束并同时改进基于三维AAM的分割。我们从33名受试者获取的7980幅短轴心脏MR图像上取得了令人鼓舞的结果（心内膜/心外膜误差为1.43±0.49毫米/1.51±0.48毫米）。我们已将我们的数据集在线发布，供社区使用和在此基础上进行构建。
