Ben Ayed Ismail, Punithakumar Kumaradevan, Minhas Rashid, Joshi Kumradvan Rohit, Garvin Gregory J
GE Healthcare, London, ON, Canada.
Med Image Comput Comput Assist Interv. 2012;15(Pt 1):520-7. doi: 10.1007/978-3-642-33415-3_64.
We state vertebral body (VB) segmentation in MRI as a distribution-matching problem, and propose a convex-relaxation solution which is amenable to parallel computations. The proposed algorithm does not require a complex learning from a large manually-built training set, as is the case of the existing methods. From a very simple user input, which amounts to only three points for a whole volume, we compute a multi-dimensional model distribution of features that encode contextual information about the VBs. Then, we optimize a functional containing (1) a feature-based constraint which evaluates a similarity between distributions, and (2) a total-variation constraint which favors smooth surfaces. Our formulation leads to a challenging problem which is not directly amenable to convex-optimization techniques. To obtain a solution efficiently, we split the problem into a sequence of sub-problems, each can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed solution can bring a substantial speed-up of more than 30 times for a typical 3D spine MRI volume. We report quantitative performance evaluations over 15 subjects, and demonstrate that the results correlate well with independent manual segmentations.
我们将磁共振成像（MRI）中的椎体（VB）分割问题表述为一个分布匹配问题，并提出了一种适用于并行计算的凸松弛解决方案。与现有方法不同，所提出的算法不需要从大量人工构建的训练集中进行复杂的学习。仅通过非常简单的用户输入（对于整个体积仅需三个点），我们就能计算出编码椎体上下文信息的特征的多维模型分布。然后，我们优化一个泛函，该泛函包含（1）一个基于特征的约束，用于评估分布之间的相似度，以及（2）一个总变差约束，该约束有利于光滑表面。我们的公式导致了一个具有挑战性的问题，该问题不能直接用凸优化技术解决。为了有效地获得解决方案，我们将问题分解为一系列子问题，每个子问题都可以通过凸松弛和增广拉格朗日方法精确地全局求解。我们在图形处理单元（GPU）上的并行实现表明，对于典型的3D脊柱MRI体积，所提出的解决方案可以带来超过30倍的显著加速。我们报告了对15名受试者的定量性能评估，并证明结果与独立的手动分割结果具有良好的相关性。
