Albà Xènia, Figueras I Ventura Rosa M, Lekadir Karim, Tobon-Gomez Catalina, Hoogendoorn Corné, Frangi Alejandro F
Center for Computational Imaging & Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain.
Magn Reson Med. 2014 Dec;72(6):1775-84. doi: 10.1002/mrm.25079. Epub 2013 Dec 17.
Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data.
A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness.
The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach.
The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility.
磁共振成像（MRI），特别是延迟增强MRI，是评估心脏存活能力的标准临床成像方案。心肌壁的分割是该评估的前提条件。需要自动且稳健的多序列分割来支持处理大量数据。
本文提出了一种基于通用规则的框架，用于自动分割左心室心肌。我们使用强度信息，并纳入形状和层间平滑度约束，以增强对个体和研究特定变化的稳健性。我们的自动初始化考虑了左心室的几何和外观属性以及层间信息。分割算法使用一种带有控制点的解耦、改进的图割方法，在灵活性和稳健性之间实现了良好的平衡。
该方法在来自一个包含20名患者的内部数据库的延迟增强MRI图像上进行了评估，并在来自一个包含15名患者的开放获取数据库的电影MRI图像上进行了评估，两者均使用手动勾勒的轮廓作为参考。使用Dice系数测量的分割一致性，对于延迟增强MRI和电影MRI分别为0.81±0.05和0.92±0.04。该方法与三维主动形状模型方法相比也具有优势。
对两种磁共振序列的实验验证表明准确性和通用性有所提高。
