General Electric Canada (GE Healthcare), London, ON, Canada.
Int J Comput Assist Radiol Surg. 2009 Jan;4(1):37-44. doi: 10.1007/s11548-008-0265-y. Epub 2008 Oct 28.
The goal of this study is to investigate automatic myocardium tracking in cardiac Magnetic Resonance (MR) sequences using global distribution matching via level-set curve evolution. Rather than relying on the pixelwise information as in existing approaches, distribution matching compares intensity distributions, and consequently, is well-suited to the myocardium tracking problem.
Starting from a manual segmentation of the first frame, two curves are evolved in order to recover the endocardium (inner myocardium boundary) and the epicardium (outer myocardium boundary) in all the frames. For each curve, the evolution equation is sought following the maximization of a functional containing two terms: (1) a distribution matching term measuring the similarity between the non-parametric intensity distributions sampled from inside and outside the curve to the model distributions of the corresponding regions estimated from the previous frame; (2) a gradient term for smoothing the curve and biasing it toward high gradient of intensity. The Bhattacharyya coefficient is used as a similarity measure between distributions. The functional maximization is obtained by the Euler-Lagrange ascent equation of curve evolution, and efficiently implemented via level-set. The performance of the proposed distribution matching was quantitatively evaluated by comparisons with independent manual segmentations approved by an experienced cardiologist. The method was applied to ten 2D mid-cavity MR sequences corresponding to ten different subjects.
Although neither shape prior knowledge nor curve coupling were used, quantitative evaluation demonstrated that the results were consistent with manual segmentations. The proposed method compares well with existing methods. The algorithm also yields a satisfying reproducibility.
Distribution matching leads to a myocardium tracking which is more flexible and applicable than existing methods because the algorithm uses only the current data, i.e., does not require a training, and consequently, the solution is not bounded to some shape/intensity prior information learned from of a finite training set.
本研究旨在通过基于水平集曲线演化的全局分布匹配来探索心脏磁共振（MR）序列中的自动心肌跟踪。与现有方法中依赖于像素信息的方法不同，分布匹配比较强度分布，因此非常适合心肌跟踪问题。
从第一帧的手动分割开始，为了在所有帧中恢复心内膜（心肌内边界）和心外膜（心肌外边界），演化两条曲线。对于每条曲线，通过最大化包含两个项的函数来寻找演化方程：（1）分布匹配项，用于测量从曲线内外采样的非参数强度分布与从前一帧估计的对应区域的模型分布之间的相似性；（2）梯度项，用于平滑曲线并使其偏向于强度的高梯度。Bhattacharyya 系数用于度量分布之间的相似性。函数最大化通过曲线演化的欧拉-拉格朗日上升方程获得，并通过水平集高效实现。通过与经验丰富的心脏病专家认可的独立手动分割进行定量比较，评估了所提出的分布匹配的性能。该方法应用于十个对应于十个不同受试者的 2D 中腔 MR 序列。
尽管没有使用形状先验知识或曲线耦合，但定量评估表明，结果与手动分割一致。与现有方法相比，所提出的方法表现良好。该算法还具有令人满意的可重复性。
分布匹配导致的心肌跟踪比现有方法更灵活和适用，因为算法仅使用当前数据，即不需要训练，因此，解决方案不受从有限训练集中学习的某些形状/强度先验信息的限制。
