Centre for Image Processing and Analysis, Dublin City University, Dublin 9, Ireland.
IEEE Trans Med Imaging. 2011 Feb;30(2):461-74. doi: 10.1109/TMI.2010.2086465. Epub 2010 Oct 14.
A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D +time structures, such as the cardiac left ventricle, due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the "curse of dimensionality." Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epicardium contours of the left ventricle by coupling them at segmentation-time, instead of at model-time. We apply our approach to 33 3D +time cardiac MRI datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy.
一种常见的基于模型的分割方法是假设自上而下的建模策略。然而，对于复杂的 3D+时间结构，如心脏左心室，由于训练要求增加、对齐困难和模型中的局部最小值，这种方法是不可行的。作为我们的主要贡献，我们提出了一种替代的自下而上的建模方法。通过在分割时组合多个针对特定维度的模型中捕获的变化，我们创建了一个可扩展的分割框架，不会受到“维度诅咒”的影响。我们的第二个贡献涉及灵活的轮廓耦合技术，允许我们的分割方法适应训练集之外的未见轮廓配置。这用于通过在分割时而不是在模型时耦合它们来识别心脏左心室的内界膜和外界膜轮廓。我们将我们的方法应用于 33 个 3D+时间心脏 MRI 数据集，并与几个最先进的作品进行了全面评估。定量评估表明，我们的方法比最先进的基于模型的方法需要的训练量明显更少，同时保持或提高分割准确性。
